diff --git a/notebooks/crash-20200902-170910-hkebiri-Multiple_Brain_extraction-b18e8797-786c-468f-8422-39c9a03c92b7.pklz b/notebooks/crash-20200902-170910-hkebiri-Multiple_Brain_extraction-b18e8797-786c-468f-8422-39c9a03c92b7.pklz
deleted file mode 100644
index a164075f3..000000000
Binary files a/notebooks/crash-20200902-170910-hkebiri-Multiple_Brain_extraction-b18e8797-786c-468f-8422-39c9a03c92b7.pklz and /dev/null differ
diff --git a/notebooks/crash-20200902-182548-hkebiri-Multiple_Brain_extraction-94bdeb49-6c0b-47f5-9a03-ddeb8f3fe5d8.pklz b/notebooks/crash-20200902-182548-hkebiri-Multiple_Brain_extraction-94bdeb49-6c0b-47f5-9a03-ddeb8f3fe5d8.pklz
deleted file mode 100644
index 830974719..000000000
Binary files a/notebooks/crash-20200902-182548-hkebiri-Multiple_Brain_extraction-94bdeb49-6c0b-47f5-9a03-ddeb8f3fe5d8.pklz and /dev/null differ
diff --git a/pymialsrtk/interfaces/preprocess.py b/pymialsrtk/interfaces/preprocess.py
index 64504a7b3..39b411edf 100644
--- a/pymialsrtk/interfaces/preprocess.py
+++ b/pymialsrtk/interfaces/preprocess.py
@@ -694,7 +694,8 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
         img_nib = nibabel.load(os.path.join(dataPath))
         image_data = img_nib.get_data()
         images = np.zeros((image_data.shape[2], width, height, n_channels))
-        pred3dFinal = np.zeros((image_data.shape[2], width, height, n_channels))
+        #pred3dFinal = np.zeros((image_data.shape[2], width, height, n_channels))
+        pred3dFinal = np.zeros((image_data.shape[2], image_data.shape[0], image_data.shape[1], n_channels))
 
         slice_counter = 0
         for ii in range(image_data.shape[2]):
@@ -801,7 +802,7 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
             for i in range(np.asarray(pred3d).shape[0]):
                 if np.sum(pred3d[i, :, :]) != 0:
                     pred3d[i, :, :] = self._extractLargestCC(pred3d[i, :, :].astype('uint8'))
-                    contours, _ = cv2.findContours(pred3d[i, :, :].astype('uint8'), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                    contours, hierarchy = cv2.findContours(pred3d[i, :, :].astype('uint8'), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
                     area = cv2.minAreaRect(np.squeeze(contours))
                     heights.append(area[1][0])
                     widths.append(area[1][1])