MICCAI 2016 Daily - Thursday

Fully Convolutional Network for Liver Segmentation and Lesions Detection This Friday, MICCAI 2016 will host workshops that will be held after the publication of our last MICCAI Daily . Among them, DLMIA 2016 : the 2nd Workshop on Deep Learning in Medical Image Analysis . We have asked Avi Ben Cohen , one of the poster presenters at the morning session, to tell This project presents an automatic system for liver segmentation and lesions detection in CT examinations using fully convolutional neural network architecture . Currently this task is manually handled by radiologists and can be very time consuming. In addition, the variability in texture, size and different contrast enhancement behavior of liver lesions and parenchyma make it in many cases a difficult task. One of the difficulties comes from detecting small liver lesions : the model explores different scales of the fully convolutional architecture for that purpose and although in some cases this was helpful, in other cases small lesion can be missed, meaning that more improvements and work needs to be done. This project is a part of an ongoing project for creating a package of tools which is necessary for radiologists , when they go over a liver CT examination. These tools include the liver segmentation , lesion detection and segmentation , follow-up tools for treatment evaluation and classification tools for different lesion types. “ A package of tools necessary for radiologists going over a liver CT examination ” Presentation 11 us about the work he is presenting with his colleagues ( Idit Diamant , Eyal Klang, Michal Amitai and Hayit Greenspan ). The project is a part of a joint effort of the Medical Image Processing lab in Tel Aviv University (headed by Prof. Hayit Greenspan) and the Sheba Medical Center . MICCAI Daily: Thursday