[month] [year]

MICCAI-2021

Prof. C V Jawaha and his student Bhavani Sambaturu presented a poster of their paper on Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images at the 24th International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI, 2021) from 27 September – 1 October. Research work as explained by the authors:

Semantic segmentation of medical images is an essential first step in computer-aided diagnosis systems for many applications. However, modern deep neural networks (DNNs) have generally shown inconsistent performance for clinical use. This has led researchers to propose interactive image segmentation techniques where the output of a DNN can be interactively corrected by a medical expert to the desired accuracy. However, these techniques often need separate training data with the associated human interactions, and do not generalize to various diseases, and types of medical images. In this paper, we suggest a novel conditional inference technique for deep neural networks which takes the intervention by a medical expert as test time constraints and performs inference conditioned upon these constraints. Our technique is generic can be used for medical images from any modality. Unlike other methods, our approach can correct multiple structures at the same time and add structures missed at initial segmentation. We report an improvement of 13.3, 12.5, 17.8, 10.2, and 12.4 times in terms of user annotation time compared to full human annotation for the nucleus, multiple cell, liver and tumor, organ, and brain segmentation respectively. In comparison to other interactive segmentation techniques, we report a time saving of 2.8 , 3.0, 1.9, 4.4, and 8.6 fold. Our method can be useful to clinicians for diagnosis and, post-surgical follow-up with minimal intervention from the medical expert.

Abstract link: http://cvit.iiit.ac.in/research/projects/cvit-projects/semi-automatic-medical-image-annotation