PyTorch implementation of MICCAI 2018 paper "Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector"

Related tags

Deep Learningobject-detectionmedical-image-computingliver-segmentationsingle-shot-multibox-detectormedical-diagnosticsliver-disease
Overview

Grouped SSD (GSSD) for liver lesion detection from multi-phase CT

Note: the MICCAI 2018 paper only covers the multi-phase lesion detection part of this project. The liver segmentation with U-net is currently WIP.

Pixel-wise liver segmentation & multi-phase lesion detection from CT image in PyTorch.

This repo uses in-house industrial CT dataset, so the code for data loading is little nasty & unstructured, and I cannot release the dataset.

But structures of the liver segmentation data are nearly identical to 3Dircadb (http://www.ircad.fr/research/3dircadb/). And the lesion detection dataset looks like the figure 1 from our MICCAI 2018 paper.

train_liverseg_unet.py: U-net training for liver segmentation. Assumes 512x512 slices of dicom CT image and their corresponding binary segmentation masks.

ssd_liverdet/train_lesion_multiphase.py: GSSD training for liver lesion detection. Assumes 4-phase 512x512 slices of dicom CT image and their corresponding bounding box annotations.

The original SSD implementation is hard-forked from here.

Owner
Sang-gil Lee
Ph.D. student in ML/AI @ Seoul National University, South Korea. I do deep learning for sequence & generative models.
Sang-gil Lee
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