2019/07/31 by Michal Heker, Heker, Michal, Avi Ben-Cohen +3
Engineering · Medicine · #FOS: Electrical engineering #Hepatitis B Virus Studies #Hepatocellular Carcinoma Treatment and Prognosis #Image and Video Processing (eess.IV) #Radiomics and Machine Learning in Medical Imaging #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1907.13409
Accepted to IEEE EMBC 2019
arxiv created 2019/07/31 · openalex publication_date 2019/07/31 · arxiv updated 2019/08/01 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
We present an automatic method for joint liver lesion segmentation and classification using a hierarchical fine-tuning framework. Our dataset is small, containing 332 2-D CT examinations with lesion annotated into 3 lesion types: cysts, hemangiomas, and metastases. Using a cascaded U-net that performs segmentation and classification simultaneously, we trained a strong lesion segmentation model on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. We used the trained weights to fine-tune a slightly modified model to obtain improved lesion segmentation and classification, on the smaller dataset. Since pre-training was done with similar data on a related task, we were able to learn more representative features (especially higher-level features in the U-Net's encoder), and improve pixel-wise classification results. We show an improvement of over 10% in Dice score and classification accuracy, compared to a baseline model. We further improve the classification performance by hierarchically freezing the encoder part of the network and achieve an improvement of over 15% in Dice score and classification accuracy. We compare our results with an existing method and show an improvement of 14% in the success rate and 12% in the classification accuracy.