diff --git a/.gitignore b/.gitignore
index 7d403d6..2b2d6b2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -169,4 +169,8 @@ debug_data/
 !nuclei.tif
 !cytoplasm.tif
 !binary_mask_sdh.tif
-data/*
\ No newline at end of file
+data/*
+*intensity_plot.png
+*.idea
+cellpose.sh
+nohup.out
diff --git a/.python-version b/.python-version
index 54c5196..09dcc78 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.10.9
+3.10.11
diff --git a/.vscode/launch.json b/.vscode/launch.json
index a970ca1..f11f1a9 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -26,7 +26,7 @@
             "request": "launch",
             "module": "myoquant",
             "justMyCode": true,
-            "args": ["atp-analysis", "sample_img/sample_atp.jpg", "--cellpose-path", "sample_img/sample_atp_cellpose_mask.tiff"],
+            "args": ["atp-analysis", "sample_img/sample_atp.jpg", "--cellpose-path", "sample_img/sample_atp_cellpose_mask.tiff", "--intensity-method", "mean", "--n-classes", "2", "--erosion"],
         }
     ]
 }
\ No newline at end of file
diff --git a/CLI_Documentation.md b/CLI_Documentation.md
index 48f0147..53301ff 100644
--- a/CLI_Documentation.md
+++ b/CLI_Documentation.md
@@ -10,6 +10,7 @@ $ myoquant [OPTIONS] COMMAND [ARGS]...
 
 **Options**:
 
+- `--version`
 - `--help`: Show this message and exit.
 
 **Commands**:
@@ -43,6 +44,12 @@ $ myoquant atp-analysis [OPTIONS] IMAGE_PATH
 - `--output-path PATH`: The path to the folder to save the results. Will save in the same folder as input image if not specified.
 - `--intensity-threshold INTEGER RANGE`: Fiber intensity threshold to differenciate between the two fiber types. If not specified, the analysis will try to deduce it. [1<=x<=254]
 - `--cellpose-diameter INTEGER`: Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.
+- `--channel INTEGER`: Image channel to use for the analysis. If not specified, the analysis will be performed on all three channels.
+- `--channel-first / --no-channel-first`: If the channel is the first dimension of the image, set this to True. False by default. [default: no-channel-first]
+- `--rescale-exposure / --no-rescale-exposure`: Rescale the image exposure if your image is not in the 0 255 forma, False by default. [default: no-rescale-exposure]
+- `--n-classes INTEGER RANGE`: The number of classes of cell to detect. If not specified this is defaulted to two classes. [default: 2; x<=10]
+- `--intensity-method TEXT`: The method to use to compute the intensity of the cell. Can be either 'median' or 'mean'. [default: median]
+- `--erosion INTEGER RANGE`: Perform an erosion on the cells images to remove signal in the cell membrane (usefull for fluo). Expressed in percentage of the cell radius [default: False; x<=45]
 - `--export-map / --no-export-map`: Export the original image with cells painted by classification label. [default: export-map]
 - `--export-stats / --no-export-stats`: Export per fiber and per nuclei stat table. [default: export-stats]
 - `--help`: Show this message and exit.
diff --git a/myoquant/__main__.py b/myoquant/__main__.py
index 2c6d1cf..26bc77f 100644
--- a/myoquant/__main__.py
+++ b/myoquant/__main__.py
@@ -1,11 +1,27 @@
 import typer
 from rich.console import Console
+import pkg_resources
+
+__version__ = pkg_resources.get_distribution("myoquant").version
+__version_cellpose__ = pkg_resources.get_distribution("cellpose").version
+__version_stardist__ = pkg_resources.get_distribution("stardist").version
+__version_torch__ = pkg_resources.get_distribution("torch").version
+__version_tensorflow__ = pkg_resources.get_distribution("tensorflow").version
 
 from .commands.docs import app as docs_app
 from .commands import run_sdh, run_he, run_atp
 
 console = Console()
 
+
+def version_callback(value: bool):
+    if value:
+        print(
+            f"MyoQuant Version: {__version__} \nCellpose Version: {__version_cellpose__} \nStardist Version: {__version_stardist__} \nTorch Version: {__version_torch__} \nTensorflow Version: {__version_tensorflow__}"
+        )
+        raise typer.Exit()
+
+
 app = typer.Typer(
     name="MyoQuant",
     add_completion=False,
@@ -15,6 +31,17 @@
 app.add_typer(docs_app, name="docs", help="Generate documentation")
 
 
+@app.callback()
+def main(
+    version: bool = typer.Option(
+        None, "--version", callback=version_callback, is_eager=True
+    )
+):
+    """
+    MyoQuant Analysis Command Line Interface
+    """
+
+
 app.registered_commands += (
     run_sdh.app.registered_commands
     + run_he.app.registered_commands
diff --git a/myoquant/commands/run_atp.py b/myoquant/commands/run_atp.py
index 6cde6b6..881e745 100644
--- a/myoquant/commands/run_atp.py
+++ b/myoquant/commands/run_atp.py
@@ -59,6 +59,32 @@ def atp_analysis(
         None,
         help="Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.",
     ),
+    channel: int = typer.Option(
+        None,
+        help="Image channel to use for the analysis. If not specified, the analysis will be performed on all three channels.",
+    ),
+    channel_first: bool = typer.Option(
+        False,
+        help="If the channel is the first dimension of the image, set this to True. False by default.",
+    ),
+    rescale_exposure: bool = typer.Option(
+        False,
+        help="Rescale the image exposure if your image is not in the 0 255 forma, False by default.",
+    ),
+    n_classes: int = typer.Option(
+        2,
+        max=10,
+        help="The number of classes of cell to detect. If not specified this is defaulted to two classes.",
+    ),
+    intensity_method: str = typer.Option(
+        "median",
+        help="The method to use to compute the intensity of the cell. Can be either 'median' or 'mean'.",
+    ),
+    erosion: int = typer.Option(
+        False,
+        max=45,
+        help="Perform an erosion on the cells images to remove signal in the cell membrane (usefull for fluo). Expressed in percentage of the cell radius",
+    ),
     export_map: bool = typer.Option(
         True,
         help="Export the original image with cells painted by classification label.",
@@ -110,6 +136,7 @@ def atp_analysis(
         from ..src.ATP_analysis import run_atp_analysis
         import numpy as np
         from PIL import Image
+        from skimage.exposure import rescale_intensity
 
         try:
             from imageio.v2 import imread
@@ -135,7 +162,17 @@ def atp_analysis(
     ) as progress:
         progress.add_task(description="Reading all inputs...", total=None)
         image_ndarray = imread(image_path)
-
+        if channel is not None:
+            if channel_first:
+                # Put the channel as third dimension instead of first
+                image_ndarray = np.moveaxis(image_ndarray, 0, -1)
+            image_ndarray = image_ndarray[:, :, channel]
+        if rescale_exposure:
+            image_ndarray = rescale_intensity(
+                image_ndarray,
+                in_range=(np.amin(image_ndarray), np.amax(image_ndarray)),
+                out_range=np.uint8,
+            )
         if mask_path is not None:
             mask_ndarray = imread(mask_path)
             if np.unique(mask_ndarray).shape[0] != 2:
@@ -200,8 +237,13 @@ def atp_analysis(
         transient=False,
     ) as progress:
         progress.add_task(description="Detecting fiber types...", total=None)
-        result_df, full_label_map, df_cellpose_details = run_atp_analysis(
-            image_ndarray, mask_cellpose, intensity_threshold
+        result_df, full_label_map, df_cellpose_details, fig = run_atp_analysis(
+            image_ndarray,
+            mask_cellpose,
+            intensity_threshold,
+            n_classes,
+            intensity_method,
+            erosion,
         )
     if export_map:
         with Progress(
@@ -214,7 +256,12 @@ def atp_analysis(
                 description="Blending label and original image together...", total=None
             )
             labelRGB_map = label2rgb(image_ndarray, full_label_map)
-            overlay_img = blend_image_with_label(image_ndarray, labelRGB_map)
+            if channel is not None:
+                overlay_img = blend_image_with_label(
+                    image_ndarray, labelRGB_map, fluo=True
+                )
+            else:
+                overlay_img = blend_image_with_label(image_ndarray, labelRGB_map)
             overlay_filename = image_path.stem + "_label_blend.tiff"
             overlay_img.save(output_path / overlay_filename)
 
@@ -239,6 +286,11 @@ def atp_analysis(
         f"💾 OUTPUT: Summary Table saved as {output_path/csv_name}",
         style="green",
     )
+    plot_name = image_path.stem + "_intensity_plot.png"
+    fig.savefig(output_path / plot_name)
+    console.print(
+        f"💾 OUTPUT: Intensity Plot saved as {output_path/plot_name}", style="green"
+    )
     if export_map:
         console.print(
             f"💾 OUTPUT: Overlay image saved as {output_path/overlay_filename}",
diff --git a/myoquant/src/ATP_analysis.py b/myoquant/src/ATP_analysis.py
index 452e239..e11fbf7 100644
--- a/myoquant/src/ATP_analysis.py
+++ b/myoquant/src/ATP_analysis.py
@@ -3,70 +3,115 @@
 from sklearn.mixture import GaussianMixture
 from .common_func import extract_single_image, df_from_cellpose_mask
 import numpy as np
+import matplotlib
+
+matplotlib.use("agg")
+
 import matplotlib.pyplot as plt
 
 labels_predict = {1: "fiber type 1", 2: "fiber type 2"}
 np.random.seed(42)
 
 
-def get_all_intensity(image_array, df_cellpose):
+def get_all_intensity(
+    image_array, df_cellpose, intensity_method="median", erosion=None
+):
     all_cell_median_intensity = []
     for index in range(len(df_cellpose)):
-        single_cell_img = extract_single_image(image_array, df_cellpose, index)
+        single_cell_img = extract_single_image(image_array, df_cellpose, index, erosion)
 
         # Calculate median pixel intensity of the cell but ignore 0 values
-        single_cell_median_intensity = np.median(single_cell_img[single_cell_img > 0])
+        if intensity_method == "median":
+            single_cell_median_intensity = np.median(
+                single_cell_img[single_cell_img > 0]
+            )
+        elif intensity_method == "mean":
+            single_cell_median_intensity = np.mean(single_cell_img[single_cell_img > 0])
         all_cell_median_intensity.append(single_cell_median_intensity)
     return all_cell_median_intensity
 
 
-def estimate_threshold(intensity_list):
+def estimate_threshold(intensity_list, n_classes=2):
     density = gaussian_kde(intensity_list)
-    density.covariance_factor = lambda: 0.25
+    density.covariance_factor = lambda: 0.05
     density._compute_covariance()
 
     # Create a vector of 256 values going from 0 to 256:
     xs = np.linspace(0, 255, 256)
     density_xs_values = density(xs)
-    gmm = GaussianMixture(n_components=2).fit(np.array(intensity_list).reshape(-1, 1))
+    gmm = GaussianMixture(n_components=n_classes).fit(
+        np.array(intensity_list).reshape(-1, 1)
+    )
 
     # Find the x values of the two peaks
     peaks_x = np.sort(gmm.means_.flatten())
     # Find the minimum point between the two peaks
-    min_index = np.argmin(density_xs_values[(xs > peaks_x[0]) & (xs < peaks_x[1])])
-    threshold = peaks_x[0] + xs[min_index]
 
-    return threshold
+    threshold_list = []
+    length = len(peaks_x)
+    for index, peaks in enumerate(peaks_x):
+        if index == length - 1:
+            break
+        min_index = np.argmin(
+            density_xs_values[(xs > peaks) & (xs < peaks_x[index + 1])]
+        )
+        threshold_list.append(peaks + xs[min_index])
+    return threshold_list
 
 
-def plot_density(all_cell_median_intensity, intensity_threshold):
+def plot_density(all_cell_median_intensity, intensity_threshold, n_classes=2):
     if intensity_threshold == 0:
-        intensity_threshold = estimate_threshold(all_cell_median_intensity)
+        intensity_threshold = estimate_threshold(all_cell_median_intensity, n_classes)
     fig, ax = plt.subplots(figsize=(10, 5))
     density = gaussian_kde(all_cell_median_intensity)
-    density.covariance_factor = lambda: 0.25
+    density.covariance_factor = lambda: 0.1
     density._compute_covariance()
 
-    # Create a vector of 256 values going from 0 to 256:
+    # Create a vector of 256 values going from 0 to 25
     xs = np.linspace(0, 255, 256)
     density_xs_values = density(xs)
-    ax.plot(xs, density_xs_values, label="Estimated Density")
-    ax.axvline(x=intensity_threshold, color="red", label="Threshold")
+    ax.hist(
+        all_cell_median_intensity, bins=255, density=True, alpha=0.5, label="Histogram"
+    )
+    ax.plot(xs, density_xs_values, label="Estimated Density", linewidth=3)
+    for values in intensity_threshold:
+        ax.axvline(x=values, color="red", label="Threshold")
     ax.set_xlabel("Pixel Intensity")
     ax.set_ylabel("Density")
     ax.legend()
     return fig
 
 
-def predict_all_cells(histo_img, cellpose_df, intensity_threshold):
-    all_cell_median_intensity = get_all_intensity(histo_img, cellpose_df)
+def merge_peaks_too_close(peak_list):
+    pass
+
+
+def classify_cells_intensity(all_cell_median_intensity, intensity_threshold):
+    muscle_fiber_type_all = []
+    for intensity in all_cell_median_intensity:
+        class_cell = np.searchsorted(intensity_threshold, intensity, side="right")
+        muscle_fiber_type_all.append(class_cell)
+    return muscle_fiber_type_all
+
+
+def predict_all_cells(
+    histo_img,
+    cellpose_df,
+    intensity_threshold,
+    n_classes=2,
+    intensity_method="median",
+    erosion=None,
+):
+    all_cell_median_intensity = get_all_intensity(
+        histo_img, cellpose_df, intensity_method, erosion
+    )
     if intensity_threshold is None:
-        intensity_threshold = estimate_threshold(all_cell_median_intensity)
+        intensity_threshold = estimate_threshold(all_cell_median_intensity, n_classes)
 
-    muscle_fiber_type_all = [
-        1 if x > intensity_threshold else 2 for x in all_cell_median_intensity
-    ]
-    return muscle_fiber_type_all, all_cell_median_intensity
+    muscle_fiber_type_all = classify_cells_intensity(
+        all_cell_median_intensity, intensity_threshold
+    )
+    return muscle_fiber_type_all, all_cell_median_intensity, intensity_threshold
 
 
 def paint_full_image(image_atp, df_cellpose, class_predicted_all):
@@ -76,24 +121,46 @@ def paint_full_image(image_atp, df_cellpose, class_predicted_all):
     # for index in track(range(len(df_cellpose)), description="Painting cells"):
     for index in range(len(df_cellpose)):
         single_cell_mask = df_cellpose.iloc[index, 9].copy()
-        if class_predicted_all[index] == 1:
-            image_atp_paint[
-                df_cellpose.iloc[index, 5] : df_cellpose.iloc[index, 7],
-                df_cellpose.iloc[index, 6] : df_cellpose.iloc[index, 8],
-            ][single_cell_mask] = 1
-        elif class_predicted_all[index] == 2:
-            image_atp_paint[
-                df_cellpose.iloc[index, 5] : df_cellpose.iloc[index, 7],
-                df_cellpose.iloc[index, 6] : df_cellpose.iloc[index, 8],
-            ][single_cell_mask] = 2
+        image_atp_paint[
+            df_cellpose.iloc[index, 5] : df_cellpose.iloc[index, 7],
+            df_cellpose.iloc[index, 6] : df_cellpose.iloc[index, 8],
+        ][single_cell_mask] = (
+            class_predicted_all[index] + 1
+        )
     return image_atp_paint
 
 
-def run_atp_analysis(image_array, mask_cellpose, intensity_threshold=None):
+def label_list_from_threhsold(threshold_list):
+    label_list = []
+    length = len(threshold_list)
+    for index, threshold in enumerate(threshold_list):
+        if index == 0:
+            label_list.append(f"<{threshold}")
+        if index == length - 1:
+            label_list.append(f">{threshold}")
+        else:
+            label_list.append(f">{threshold} & <{threshold_list[index+1]}")
+    return label_list
+
+
+def run_atp_analysis(
+    image_array,
+    mask_cellpose,
+    intensity_threshold=None,
+    n_classes=2,
+    intensity_method="median",
+    erosion=None,
+):
     df_cellpose = df_from_cellpose_mask(mask_cellpose)
-    class_predicted_all, intensity_all = predict_all_cells(
-        image_array, df_cellpose, intensity_threshold
+    class_predicted_all, intensity_all, intensity_threshold = predict_all_cells(
+        image_array,
+        df_cellpose,
+        intensity_threshold,
+        n_classes,
+        intensity_method,
+        erosion,
     )
+    fig = plot_density(intensity_all, intensity_threshold, n_classes)
     df_cellpose["muscle_cell_type"] = class_predicted_all
     df_cellpose["cell_intensity"] = intensity_all
     count_per_label = np.unique(class_predicted_all, return_counts=True)
@@ -102,10 +169,11 @@ def run_atp_analysis(image_array, mask_cellpose, intensity_threshold=None):
     headers = ["Feature", "Raw Count", "Proportion (%)"]
     data = []
     data.append(["Muscle Fibers", len(class_predicted_all), 100])
+    label_list = label_list_from_threhsold(intensity_threshold)
     for index, elem in enumerate(count_per_label[0]):
         data.append(
             [
-                labels_predict[int(elem)],
+                label_list[int(elem)],
                 count_per_label[1][int(index)],
                 100 * count_per_label[1][int(index)] / len(class_predicted_all),
             ]
@@ -113,4 +181,4 @@ def run_atp_analysis(image_array, mask_cellpose, intensity_threshold=None):
     result_df = pd.DataFrame(columns=headers, data=data)
     # Paint The Full Image
     full_label_map = paint_full_image(image_array, df_cellpose, class_predicted_all)
-    return result_df, full_label_map, df_cellpose
+    return result_df, full_label_map, df_cellpose, fig
diff --git a/myoquant/src/common_func.py b/myoquant/src/common_func.py
index 5252f5c..8fedc65 100644
--- a/myoquant/src/common_func.py
+++ b/myoquant/src/common_func.py
@@ -5,6 +5,8 @@
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 import sys
+import math
+
 import tensorflow as tf
 import torch
 from cellpose.models import Cellpose
@@ -14,11 +16,14 @@
 import numpy as np
 from PIL import Image
 from skimage.measure import regionprops_table
+from skimage.morphology import binary_erosion
 import pandas as pd
 
 # from .gradcam import make_gradcam_heatmap, save_and_display_gradcam
 from .random_brightness import RandomBrightness
 
+import imageio
+
 tf.random.set_seed(42)
 np.random.seed(42)
 
@@ -168,12 +173,23 @@ def df_from_stardist_mask(mask, intensity_image=None):
     return df_stardist
 
 
-def extract_single_image(raw_image, df_props, index):
+def extract_single_image(raw_image, df_props, index, erosion=None):
     single_entity_img = raw_image[
         df_props.iloc[index, 5] : df_props.iloc[index, 7],
         df_props.iloc[index, 6] : df_props.iloc[index, 8],
     ].copy()
-    single_entity_mask = df_props.iloc[index, 9]
+    surface_area = df_props.iloc[index, 1]
+    cell_radius = math.sqrt(surface_area / math.pi)
+    single_entity_mask = df_props.iloc[index, 9].copy()
+    if erosion is not None:
+        erosion_size = cell_radius * (
+            erosion / 100
+        )  # Erosion in percentage of the cell radius
+        for i in range(int(erosion_size)):
+            single_entity_mask = binary_erosion(
+                single_entity_mask, out=single_entity_mask
+            )
+
     single_entity_img[~single_entity_mask] = 0
     return single_entity_img
 
@@ -192,6 +208,14 @@ def label2rgb(img_ndarray, label_map):
         0: [255, 255, 255],
         1: [15, 157, 88],
         2: [219, 68, 55],
+        3: [100, 128, 170],
+        4: [231, 204, 143],
+        5: [202, 137, 115],
+        6: [178, 143, 172],
+        7: [144, 191, 207],
+        8: [148, 187, 187],
+        9: [78, 86, 105],
+        10: [245, 90, 87],
     }
     img_rgb = np.zeros((img_ndarray.shape[0], img_ndarray.shape[1], 3), dtype=np.uint8)
 
diff --git a/notebooks/atp_exploration.ipynb b/notebooks/atp_exploration.ipynb
index 0197859..a7f8b2d 100644
--- a/notebooks/atp_exploration.ipynb
+++ b/notebooks/atp_exploration.ipynb
@@ -18,12 +18,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ATP_IMAGE_DF = df[df[\"Staining method\"] == \"ATP 9.4\"]\n",
-    "ATP_IMAGE_NAME = ATP_IMAGE_DF[\"Number\"].tolist()\n",
     "IMAGE_INDEX = 189\n",
     "# show the first image in the list using the image name\n",
     "from IPython.display import Image\n",
-    "Image(filename='../data/muscle_atlas/images/' + ATP_IMAGE_NAME[IMAGE_INDEX])"
+    "Image(filename='../sample_img/sample_atp.jpg')"
    ]
   },
   {
@@ -39,7 +37,7 @@
     "except ImportError:\n",
     "    from imageio import imread\n",
     "\n",
-    "image_array = imread('../data/muscle_atlas/images/' + ATP_IMAGE_NAME[IMAGE_INDEX])\n",
+    "image_array = imread('../sample_img/sample_atp.jpg')\n",
     "model_cellpose = load_cellpose()\n",
     "mask_cellpose = run_cellpose(image_array, model_cellpose)\n",
     "plt.imshow(mask_cellpose)"
@@ -92,6 +90,19 @@
     "    all_cell_median_intensity.append(single_cell_median_intensity)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Histogram plot of the median pixel intensity of all cells\n",
+    "plt.hist(all_cell_median_intensity, bins=255, density=True, alpha=0.5)\n",
+    "plt.plot(xs,density(xs))\n",
+    "plt.xlim(50,220)\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -103,7 +114,7 @@
     "# Build a \"density\" function based on the dataset\n",
     "# When you give a value from the X axis to this function, it returns the according value on the Y axis\n",
     "density = gaussian_kde(all_cell_median_intensity)\n",
-    "density.covariance_factor = lambda : .25\n",
+    "density.covariance_factor = lambda : .05\n",
     "density._compute_covariance()\n",
     "\n",
     "# Create a vector of 256 values going from 0 to 256:\n",
@@ -115,10 +126,19 @@
     "# Make the chart\n",
     "# We're actually building a line chart where x values are set all along the axis and y value are\n",
     "# the corresponding values from the density function\n",
+    "\n",
     "plt.plot(xs,density(xs))\n",
+    "plt.xlim(50,220)\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -179,7 +199,9 @@
     "threshold = sorted_peaks[0]+xs[min_index]\n",
     "print(threshold)\n",
     "# Plot the data\n",
-    "plt.plot(xs, density(xs), label='Density')\n",
+    "plt.hist(all_cell_median_intensity, bins=255, density=True, alpha=0.5, label='Histogram')\n",
+    "plt.plot(xs,density(xs), label='Density', linewidth=3)\n",
+    "plt.xlim(50,220)\n",
     "plt.axvline(threshold, color='r', label='Threshold')\n",
     "plt.legend()\n",
     "plt.show()"
diff --git a/poetry.lock b/poetry.lock
index ff85c1c..0bf3b92 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -91,34 +91,48 @@ files = [
 
 [[package]]
 name = "black"
-version = "22.12.0"
+version = "23.1.0"
 description = "The uncompromising code formatter."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "black-23.1.0-py3-none-any.whl", hash = "sha256:7a0f701d314cfa0896b9001df70a530eb2472babb76086344e688829efd97d32"},
+    {file = "black-23.1.0.tar.gz", hash = "sha256:b0bd97bea8903f5a2ba7219257a44e3f1f9d00073d6cc1add68f0beec69692ac"},
 ]
 
 [package.dependencies]
 click = ">=8.0.0"
 ipython = {version = ">=7.8.0", optional = true, markers = "extra == \"jupyter\""}
 mypy-extensions = ">=0.4.3"
+packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
 tokenize-rt = {version = ">=3.2.0", optional = true, markers = "extra == \"jupyter\""}
-tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
@@ -491,21 +505,17 @@ test-no-images = ["pytest"]
 
 [[package]]
 name = "csbdeep"
-version = "0.7.2"
+version = "0.7.3"
 description = "CSBDeep - a toolbox for Content-aware Image Restoration (CARE)"
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "csbdeep-0.7.3-py2.py3-none-any.whl", hash = "sha256:759340b16af1d67ecdcf20eeb680ac6f30e75fd3655847a100724473ac110197"},
+    {file = "csbdeep-0.7.3.tar.gz", hash = "sha256:e4874f37fd932b9cba60c9ec27e5b088d1bb7b09e36b16a158476371cbbffa5a"},
 ]
 
 [package.dependencies]
-h5py = [
-    {version = "<3", markers = "python_version < \"3.9\""},
-    {version = ">=3", markers = "python_version >= \"3.9\""},
-]
 matplotlib = "*"
 numpy = "*"
 packaging = "*"
@@ -583,18 +593,6 @@ files = [
     {file = "distlib-0.3.6.tar.gz", hash = "sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46"},
 ]
 
-[[package]]
-name = "entrypoints"
-version = "0.4"
-description = "Discover and load entry points from installed packages."
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"},
-    {file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"},
-]
-
 [[package]]
 name = "exceptiongroup"
 version = "1.1.0"
@@ -627,46 +625,46 @@ tests = ["asttokens", "littleutils", "pytest", "rich"]
 
 [[package]]
 name = "fastremap"
-version = "1.13.3"
+version = "1.13.4"
 description = "Remap, mask, renumber, unique, and in-place transposition of 3D labeled images. Point cloud too."
 category = "main"
 optional = false
 python-versions = "~=3.6"
 files = [
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 [package.dependencies]
@@ -874,49 +872,6 @@ files = [
 [package.extras]
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-[[package]]
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-description = "Read and write HDF5 files from Python"
-category = "main"
-optional = false
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-]
-
-[package.dependencies]
-numpy = ">=1.7"
-six = "*"
-
 [[package]]
 name = "h5py"
 version = "3.8.0"
@@ -952,14 +907,14 @@ numpy = ">=1.14.5"
 
 [[package]]
 name = "identify"
-version = "2.5.15"
+version = "2.5.18"
 description = "File identification library for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -1019,14 +974,14 @@ all = ["matplotlib", "numcodecs", "tifffile"]
 
 [[package]]
 name = "imageio"
-version = "2.25.0"
+version = "2.25.1"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1070,6 +1025,25 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker
 perf = ["ipython"]
 testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"]
 
+[[package]]
+name = "importlib-resources"
+version = "5.10.2"
+description = "Read resources from Python packages"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "importlib_resources-5.10.2-py3-none-any.whl", hash = "sha256:7d543798b0beca10b6a01ac7cafda9f822c54db9e8376a6bf57e0cbd74d486b6"},
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+]
+
+[package.dependencies]
+zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -1084,27 +1058,28 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.20.2"
+version = "6.21.2"
 description = "IPython Kernel for Jupyter"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "ipykernel-6.21.2.tar.gz", hash = "sha256:6e9213484e4ce1fb14267ee435e18f23cc3a0634e635b9fb4ed4677b84e0fdf8"},
 ]
 
 [package.dependencies]
 appnope = {version = "*", markers = "platform_system == \"Darwin\""}
 comm = ">=0.1.1"
-debugpy = ">=1.0"
+debugpy = ">=1.6.5"
 ipython = ">=7.23.1"
 jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
 matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
 psutil = "*"
-pyzmq = ">=17"
+pyzmq = ">=20"
 tornado = ">=6.1"
 traitlets = ">=5.4.0"
 
@@ -1117,14 +1092,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
-version = "8.8.0"
+version = "8.10.0"
 description = "IPython: Productive Interactive Computing"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "ipython-8.10.0-py3-none-any.whl", hash = "sha256:b38c31e8fc7eff642fc7c597061fff462537cf2314e3225a19c906b7b0d8a345"},
+    {file = "ipython-8.10.0.tar.gz", hash = "sha256:b13a1d6c1f5818bd388db53b7107d17454129a70de2b87481d555daede5eb49e"},
 ]
 
 [package.dependencies]
@@ -1136,13 +1111,13 @@ jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
 pickleshare = "*"
-prompt-toolkit = ">=3.0.11,<3.1.0"
+prompt-toolkit = ">=3.0.30,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.20)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
 doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
@@ -1152,7 +1127,7 @@ notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
 test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.20)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
 
 [[package]]
 name = "jedi"
@@ -1206,39 +1181,38 @@ files = [
 
 [[package]]
 name = "jupyter-client"
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 [package.dependencies]
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 fonttools = ">=4.22.0"
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 pillow = ">=6.2.0"
-pyparsing = ">=2.2.1"
+pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
 [[package]]
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 [[package]]
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 [package.dependencies]
@@ -1753,14 +1724,14 @@ reports = ["lxml"]
 
 [[package]]
 name = "mypy-extensions"
-version = "0.4.3"
-description = "Experimental type system extensions for programs checked with the mypy typechecker."
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.5"
 files = [
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 [[package]]
@@ -2113,14 +2084,14 @@ testing = ["docopt", "pytest (<6.0.0)"]
 
 [[package]]
 name = "pathspec"
-version = "0.10.3"
+version = "0.11.0"
 description = "Utility library for gitignore style pattern matching of file paths."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [[package]]
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@@ -2243,19 +2207,19 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 
 [[package]]
 name = "platformdirs"
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+version = "3.0.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.2.2)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
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+test = ["appdirs (==1.4.4)", "covdefaults (>=2.2.2)", "pytest (>=7.2.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
 
 [[package]]
 name = "pluggy"
@@ -2275,14 +2239,14 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pre-commit"
-version = "3.0.0"
+version = "3.0.4"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -2791,23 +2755,23 @@ rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 
 [[package]]
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-version = "13.2.0"
+version = "13.3.1"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
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 [package.dependencies]
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 typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.9\""}
 
 [package.extras]
-jupyter = ["ipywidgets (>=7.5.1,<8.0.0)"]
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
 name = "rsa"
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 category = "dev"
 optional = false
 python-versions = ">=3.7"
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 [[package]]
@@ -2904,33 +2869,33 @@ test = ["asv", "codecov", "flake8", "matplotlib (>=3.0.3)", "pooch (>=1.3.0)", "
 
 [[package]]
 name = "scikit-learn"
-version = "1.2.0"
+version = "1.2.1"
 description = "A set of python modules for machine learning and data mining"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3008,14 +2973,14 @@ stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"]
 
 [[package]]
 name = "setuptools"
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 ]
 
 [package.extras]
@@ -3270,14 +3235,14 @@ files = [
 
 [[package]]
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+version = "2023.2.3"
 description = "Read and write TIFF files"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tifffile-2023.1.23.1-py3-none-any.whl", hash = "sha256:2b626a8ee81b692e1fa5320201a4b70f1915d2567945e68665621a75a12b3730"},
-    {file = "tifffile-2023.1.23.1.tar.gz", hash = "sha256:a3d0c149cc7faef796fd598856314f43fdcd0895c79c3cb662a38c1c1bd49572"},
+    {file = "tifffile-2023.2.3-py3-none-any.whl", hash = "sha256:6df45c1eab632eadb9384994c8ae8bc424cd3c26e9508481a345dbef1dcfd480"},
+    {file = "tifffile-2023.2.3.tar.gz", hash = "sha256:458df5ad9a5217f668edd636dc19fbc736062bf78aac823ab84cdbae9de8eaa6"},
 ]
 
 [package.dependencies]
@@ -3395,14 +3360,14 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.8.1"
+version = "5.9.0"
 description = "Traitlets Python configuration system"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "traitlets-5.8.1-py3-none-any.whl", hash = "sha256:a1ca5df6414f8b5760f7c5f256e326ee21b581742114545b462b35ffe3f04861"},
-    {file = "traitlets-5.8.1.tar.gz", hash = "sha256:32500888f5ff7bbf3b9267ea31748fa657aaf34d56d85e60f91dda7dc7f5785b"},
+    {file = "traitlets-5.9.0-py3-none-any.whl", hash = "sha256:9e6ec080259b9a5940c797d58b613b5e31441c2257b87c2e795c5228ae80d2d8"},
+    {file = "traitlets-5.9.0.tar.gz", hash = "sha256:f6cde21a9c68cf756af02035f72d5a723bf607e862e7be33ece505abf4a3bad9"},
 ]
 
 [package.extras]
@@ -3432,14 +3397,14 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=5.2,<6.0)", "isort (>=5.0.6,<6.
 
 [[package]]
 name = "typing-extensions"
-version = "4.4.0"
+version = "4.5.0"
 description = "Backported and Experimental Type Hints for Python 3.7+"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "typing_extensions-4.4.0-py3-none-any.whl", hash = "sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e"},
-    {file = "typing_extensions-4.4.0.tar.gz", hash = "sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa"},
+    {file = "typing_extensions-4.5.0-py3-none-any.whl", hash = "sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4"},
+    {file = "typing_extensions-4.5.0.tar.gz", hash = "sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb"},
 ]
 
 [[package]]
@@ -3461,24 +3426,24 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.17.1"
+version = "20.19.0"
 description = "Virtual Python Environment builder"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.17.1-py3-none-any.whl", hash = "sha256:ce3b1684d6e1a20a3e5ed36795a97dfc6af29bc3970ca8dab93e11ac6094b3c4"},
-    {file = "virtualenv-20.17.1.tar.gz", hash = "sha256:f8b927684efc6f1cc206c9db297a570ab9ad0e51c16fa9e45487d36d1905c058"},
+    {file = "virtualenv-20.19.0-py3-none-any.whl", hash = "sha256:54eb59e7352b573aa04d53f80fc9736ed0ad5143af445a1e539aada6eb947dd1"},
+    {file = "virtualenv-20.19.0.tar.gz", hash = "sha256:37a640ba82ed40b226599c522d411e4be5edb339a0c0de030c0dc7b646d61590"},
 ]
 
 [package.dependencies]
 distlib = ">=0.3.6,<1"
 filelock = ">=3.4.1,<4"
-platformdirs = ">=2.4,<3"
+platformdirs = ">=2.4,<4"
 
 [package.extras]
-docs = ["proselint (>=0.13)", "sphinx (>=5.3)", "sphinx-argparse (>=0.3.2)", "sphinx-rtd-theme (>=1)", "towncrier (>=22.8)"]
-testing = ["coverage (>=6.2)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=21.3)", "pytest (>=7.0.1)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.2)", "pytest-mock (>=3.6.1)", "pytest-randomly (>=3.10.3)", "pytest-timeout (>=2.1)"]
+docs = ["furo (>=2022.12.7)", "proselint (>=0.13)", "sphinx (>=6.1.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=22.12)"]
+test = ["covdefaults (>=2.2.2)", "coverage (>=7.1)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23)", "pytest (>=7.2.1)", "pytest-env (>=0.8.1)", "pytest-freezegun (>=0.4.2)", "pytest-mock (>=3.10)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)"]
 
 [[package]]
 name = "watchdog"
@@ -3535,14 +3500,14 @@ files = [
 
 [[package]]
 name = "werkzeug"
-version = "2.2.2"
+version = "2.2.3"
 description = "The comprehensive WSGI web application library."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Werkzeug-2.2.2-py3-none-any.whl", hash = "sha256:f979ab81f58d7318e064e99c4506445d60135ac5cd2e177a2de0089bfd4c9bd5"},
-    {file = "Werkzeug-2.2.2.tar.gz", hash = "sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f"},
+    {file = "Werkzeug-2.2.3-py3-none-any.whl", hash = "sha256:56433961bc1f12533306c624f3be5e744389ac61d722175d543e1751285da612"},
+    {file = "Werkzeug-2.2.3.tar.gz", hash = "sha256:2e1ccc9417d4da358b9de6f174e3ac094391ea1d4fbef2d667865d819dfd0afe"},
 ]
 
 [package.dependencies]
@@ -3642,18 +3607,18 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.11.0"
+version = "3.13.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "zipp-3.11.0-py3-none-any.whl", hash = "sha256:83a28fcb75844b5c0cdaf5aa4003c2d728c77e05f5aeabe8e95e56727005fbaa"},
-    {file = "zipp-3.11.0.tar.gz", hash = "sha256:a7a22e05929290a67401440b39690ae6563279bced5f314609d9d03798f56766"},
+    {file = "zipp-3.13.0-py3-none-any.whl", hash = "sha256:e8b2a36ea17df80ffe9e2c4fda3f693c3dad6df1697d3cd3af232db680950b0b"},
+    {file = "zipp-3.13.0.tar.gz", hash = "sha256:23f70e964bc11a34cef175bc90ba2914e1e4545ea1e3e2f67c079671883f9cb6"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
 [metadata]
diff --git a/pyproject.toml b/pyproject.toml
index 17c5ed4..4fa1821 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "myoquant"
-version = "0.3.1"
+version = "0.3.2"
 description = "MyoQuant🔬: a tool to automatically quantify pathological features in muscle fiber histology images."
 authors = ["Corentin Meyer <corentin.meyer@etu.unistra.fr>"]
 maintainers = ["Corentin Meyer <corentin.meyer@etu.unistra.fr>"]