added Segmentation and Classification codes

Segmentation/config.ini

@@ -0,0 +1,65 @@
+############################ input configuration sections
+[ct]
+path_to_search = /Local/Kidney_batch10_preprocessing_forsegmentation/img/tumor
+filename_contains =CT_
+spatial_window_size = (144, 144, 144)
+interp_order = 1
+axcodes=(A, R, S)
+
+[label]
+path_to_search = /Local/Segmentation/vignesh_correctedData/Preprocessed/lbl
+filename_contains = label_
+spatial_window_size = (144, 144, 144)
+interp_order = 0
+axcodes=(A, R, S)
+
+[masking]
+path_to_search = /Local/Segmentation/vignesh_correctedData/Preprocessed/lbl
+filename_contains = label_
+spatial_window_size = (144, 144, 144)
+interp_order = 0
+axcodes=(A, R, S)
+
+
+
+############################## system configuration sections
+[SYSTEM]
+cuda_devices = 0
+num_threads = 1
+num_gpus = 1
+model_dir = /Local/Segmentation/Final_DenseVnet/Diseased_weights
+queue_length = 36
+
+[NETWORK]
+name = dense_vnet
+batch_size = 3
+
+# volume level preprocessing
+volume_padding_size = 0
+window_sampling = resize
+
+[TRAINING]
+sample_per_volume = 4
+lr = 0.001
+loss_type = dense_vnet_abdominal_ct.dice_hinge.dice
+starting_iter =-1
+save_every_n =60
+max_iter =16000 
+rotation_angle= -10.0,10.0
+scaling_percentage=-20.0,20.0
+random_flipping_axes=1,2
+
+[INFERENCE]
+border = (0, 0, 0)
+inference_iter = 31800
+output_interp_order = 0
+spatial_window_size = (144, 144, 144)
+save_seg_dir = /Local/Kidney_batch10_preprocessing_forsegmentation/prediction/Densevnet_42000/tumor
+############################ custom configuration sections
+[SEGMENTATION]
+image = ct
+label = label
+sampler = label
+label_normalisation = False
+output_prob = False
+num_classes = 31

Segmentation/dice_hinge.py

@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+from __future__ import absolute_import, print_function, division
+
+import numpy as np
+import tensorflow as tf
+
+from niftynet.layer.base_layer import Layer
+
+
+def dice(prediction, ground_truth, weight_map=None):
+    ground_truth = tf.to_int64(ground_truth)
+    prediction = tf.cast(prediction, tf.float32)
+    ids = tf.range(tf.to_int64(tf.shape(ground_truth)[0]), dtype=tf.int64)
+    ids = tf.stack([ids, ground_truth], axis=1)
+    one_hot = tf.SparseTensor(
+        indices=ids,
+        values=tf.ones_like(ground_truth, dtype=tf.float32),
+        dense_shape=tf.to_int64(tf.shape(prediction)))
+    # if weight_map is not None:
+    #    weight_map_nclasses = tf.reshape(
+    #        tf.tile(weight_map, [n_classes]), prediction.get_shape())
+    #    dice_numerator = 2.0 * tf.sparse_reduce_sum(
+    #        weight_map_nclasses * one_hot * prediction, reduction_axes=[0])
+    # else:
+    dice_numerator = 2.0 * tf.sparse_reduce_sum(
+        one_hot * prediction, reduction_axes=[0])
+    dice_denominator = \
+        tf.reduce_sum(tf.square(prediction), reduction_indices=[0]) + \
+        tf.sparse_reduce_sum(one_hot, reduction_axes=[0])
+    epsilon_denominator = 0.00001
+
+    dice_score = dice_numerator / (dice_denominator + epsilon_denominator)
+    # dice_score.set_shape([n_classes])
+    # minimising (1 - dice_coefficients)
+    [tf.summary.scalar('Dice{}'.format(i),d) for i,d in enumerate(tf.unstack(dice_score,0))]
+    dice_score=tf.Print(dice_score,[dice_score],summarize=10,message='dice')
+    h1=tf.square(tf.minimum(0.1,dice_score)*10-1)
+    h2=tf.square(tf.minimum(0.01,dice_score)*100-1)
+
+
+    return 1.0 - tf.reduce_mean(dice_score) + \
+           tf.reduce_mean(h1)*10 + \
+           tf.reduce_mean(h2)*10
+
+
+