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IBBM / Cascaded-FCN

Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"
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TypeError: slice indices must be integers or None or have an __index__ method` #34

Open tobimichigan opened 4 years ago

tobimichigan commented 4 years ago

Firstly, let me commend you on your effort for this paper and the repo that accompanies it. As a programmer myself, I know how difficult it is to write code especially for machine learning models.

I successfully, reproduced the results through a GPU hardware and the code code ran up until:

# Visualize results imshow(img_p, lbl_p, pred>0.5, title=['Slice','Ground truth', 'Prediction'])

Besides, I also had to downgrade SCIPY to 1:00 because of the depreciated imgresize issue.

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:108: DeprecationWarning:imresizeis deprecated! imresizeis deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``skimage.transform.resize`` instead. /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:104: DeprecationWarning:imresizeis deprecated! imresizeis deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use ``skimage.transform.resize`` instead.

perhaps you could look into these issues.

Additionally, running: `# Prepare liver patch for step2

net1 output is used to determine the predicted liver bounding box

img_p2, bbox = step2_preprocess_img_slice(img_p, pred) imshow(img_p2)`

gave the following error: `--------------------------------------------------------------------------- TypeError Traceback (most recent call last)

in () ----> 1 **img_p2, bbox = step2_preprocess_img_slice(img_p, pred)** 2 imshow(img_p2) in step2_preprocess_img_slice(img_p, step1_pred) 79 y2 = min(img.shape[0], y2+y_pad) 80 ---> 81 img = img[y1:y2+1, x1:x2+1] 82 pred = pred[y1:y2+1, x1:x2+1] 83 TypeError: slice indices must be integers or None or have an __index__ method` these affected the rest of the executions such as: 1. `# Visualize result # extract liver portion as predicted by net1 x1,x2,y1,y2 = bbox lbl_p_liver = lbl_p[y1:y2,x1:x2] # Set labels to 0 and 1 lbl_p_liver[lbl_p_liver==1]=0 lbl_p_liver[lbl_p_liver==2]=1 imshow(img_p2[92:-92,92:-92],lbl_p_liver, pred2>0.5)` 2. `# Load step2 network net2 = caffe.Net(STEP2_DEPLOY_PROTOTXT, STEP2_MODEL_WEIGHTS, caffe.TEST)` and finally 3. `net2.blobs['data'].data[0,0,...] = img_p2 pred2 = net2.forward()['prob'][0,1] print (pred2.shape)` Please could you kindly proffer solutions to these issues?