Segmentation of Kidneys Deformed by Nephroblastoma Using Case-Based Reasoning

Image segmentation is a hot topic in image processing research. 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 reasoning (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 efficient every time and they often give an over-segmentation. That is why we have designed an adaptation phase which can modify the coordinates of seeds recovered during the retrieval phase. We compared our CBR approach with manual region growing and Convolutional Neural Networking (CNN) to segment kidneys and tumours of CT-scans. Our CBR system succeeded in performing the best segmentation for the kidney.