question about pre-processing and network architecture

[yaosh09]

This is a wonderful work and has great impact on 3D medical imaging deep learning.

I am trying to fine-tune Models Genesis to classify HCC pathological biomark (negative OR positive) based on liver CT data.

I'm fairly new in this field and there is a question about data pre-processing. I have abdomen CT image datas (DICOM files) and HCC ROI segmentation result (3D nii file, which value is 0 or 1 to identify the ROI region ), i don't konw how to process them to put into the model？ Merge them into one npy file?

Could you please list the network architecture of 3D Models Genesis? I plan to employ the encoder as a fixed feature extractor.

Thank you for your help,
Best Regards.

Shanhu

[MrGiovanni]

Hi @yaosh09

I have released the reference code for the target segmentation task, with data preprocessing and visualization. Processing data is not very complicated—the final input is 64x64x32 and the intensity value being [0, 1]. I also provided processed data in Google Drive for your reference.

https://github.com/MrGiovanni/ModelsGenesis/blob/master/keras/downstream_tasks/lung%20nodule%20segmentation.ipynb

For model architecture visualization, you could use Keras code
model.summary()

Thanks,
Zongwei

[yaosh09]

Hi @MrGiovanni !

My task is for binary classification.
Thank you very much for your answer.