diff --git a/MedSAM/MedSAMLite/MedSAMLite.py b/MedSAM/MedSAMLite/MedSAMLite.py
old mode 100644
new mode 100755
index f048495..264fb2a
--- a/MedSAM/MedSAMLite/MedSAMLite.py
+++ b/MedSAM/MedSAMLite/MedSAMLite.py
@@ -280,7 +280,7 @@ def setup(self) -> None:
 
         # Buttons
         self.ui.pbUpgrade.connect('clicked(bool)', lambda: self.logic.run_on_background(self.logic.upgrade, (True,), 'Checking for updates...'))
-        self.ui.pbSendImage.connect('clicked(bool)', lambda: self.logic.sendImage())
+        self.ui.pbSendImage.connect('clicked(bool)', lambda: self.logic.sendImage(partial=False))
         self.ui.pbSegment.connect('clicked(bool)', lambda: self.logic.applySegmentation())
 
         self.ui.pbCTprep.setIcon(QIcon(os.path.join(self.logic.server_dir, 'CT.jpg')))
@@ -744,9 +744,8 @@ def sendImage(self, partial=False, serverUrl='http://127.0.0.1:5555', numpyServe
         
         ###########################
         # Timer
-        if self.timer is None:
-            self.timer = QTimer()
-            self.timer.timeout.connect(lambda: self.progressCheck(partial))
+        self.timer = QTimer()
+        self.timer.timeout.connect(lambda: self.progressCheck(partial))
         ###########################
         self.progressbar = slicer.util.createProgressDialog(autoClose=False)
         self.progressbar.minimum = 0
@@ -805,7 +804,7 @@ def singleSegmentation(self, serverUrl):
     
     def applySegmentation(self, serverUrl='http://127.0.0.1:5555'):
         if self.widget.ui.pbSegment.text == 'Single Segmentation':
-            continueSingle = QMessageBox.question(None,'', "You are using single segmentation option which is faster but is not advised if you want multiple regions be segmented in one image. In that case click 'Send Image' button. Do you wish to continue with single segmentation?", QMessageBox.Yes | QMessageBox.No)
+            continueSingle = QMessageBox.question(None,'', "You are using single segmentation option which is faster but is not advised if you want large or multiple regions be segmented in one image. In that case click 'Send Image' button. Do you wish to continue with single segmentation?", QMessageBox.Yes | QMessageBox.No)
             if continueSingle == QMessageBox.No: return
             self.singleSegmentation(serverUrl)
 
diff --git a/server/server.py b/server/server.py
old mode 100644
new mode 100755
index 9b67774..07afa5b
--- a/server/server.py
+++ b/server/server.py
@@ -251,7 +251,7 @@ def get_image(wmin: int, wmax: int, zmin: int, zmax: int):
         img_1024_tensor = (
                 torch.tensor(img_1024).float().permute(2, 0, 1).unsqueeze(0).to(device)
         )
-        if (zmax == -1) or (zmin <= slice_idx <= zmax):
+        if (zmax == -1) or ((zmin-1) <= slice_idx <= (zmax+1)):
             with torch.no_grad():
                 embedding = medsam_model.image_encoder(img_1024_tensor)  # (1, 256, 64, 64)
         else:
@@ -312,8 +312,8 @@ class InferenceParams(BaseModel):
 def infer(params: InferenceParams):
     print(params.slice_idx, params.bbox, params.zrange)
     zmin, zmax = params.zrange
-    zmax += 1
-    zmin -= 1
+    zmax = min(zmax+1, len(embeddings))
+    zmin = max(zmin-1, 0)
     bbox_1024_prev = np.array([params.bbox]) / np.array([W, H, W, H]) * 256
     res = {}
     
diff --git a/server_essentials.zip b/server_essentials.zip
index 0737fab..f4a8d23 100644
Binary files a/server_essentials.zip and b/server_essentials.zip differ