Segmentation of Kidneys Deformed by Nephroblastoma using Case-Based Reasoning

International audienceImage segmentation is a hot topic in image processing re- search. Most of the time, segmentation is not fully automated, and a user is required to guide the process in order to obtain correct results. Yet, even with programs, it is a time-consuming process. In a medical context, segmentation can provide a lot of information to surgeons, but since this task is manual, it is rarely executed because of time. Artificial Intelligence (AI) is a powerful approach to create viable solutions for fully automated treatments. In this paper, we define a case-based rea- soning (CBR) that can enhance region-growing segmentation of kidneys deformed by nephroblastoma. The main problem with region-growing methods is that a user needs to place the seeds in the image manually. Automated methods exist but they are not ecient every time and they often give an over-segmentation. That is why we have designed an adap- tation phase which can modify the coordinates of seeds recovered during the retrieval phase. We compared our CBR approach with manual re- gion growing and Convolutional Neural Networking (CNN) to segment kidneys and tumours of CT-scans. Our CBR system succeeded in per- forming the best segmentation for the kidney