A fusion method based on Deep Learning and CaseBased Reasoning which improves the resulting medical image segmentations Abstract   The fusion of multiple segmentations of different biological structures is inevitable in the case where each structure has been segmented individually for performance reasons. However, when aggregating these structures for a final segmentation, conflicting pixels may appear. These conflicts can be solved by artificial intelligence techniques. Our system, integrated into the SAIAD project, carries out the fusion of deformed kidneys and nephroblastoma segmentations using the combination of Deep Learning and CaseBased Reasoning. The performances of our method were evaluated on 9 patients affected by nephroblastoma, and compared with other AI and nonAI methods adapted from the literature. The results demonstrate its effectiveness in resolving the conflicting pixels and its ability to improve the resulting segmentations.

Tumor Segmentation Based on Deeply Supervised MultiScale UNet Although deep learning has achieved great success in the field of medical image processing, the existing deep learning based medical image segmentation solutions still cannot obtain satisfactory performances for abdominal small organs and lesions due to their small object size and shapevariability. In this work, a Deeply Supervised MultiScale UNet (DSMS UNet) is proposed for more accurate segmentation performances on abdominal small organs images. DSMS UNet integrate the existing UNet model with a restoration decoder module and some multiscale convolution modules. Our experiment results demonstrate that the proposed DSMS UNet approach has much better segmentation performances than the stateoftheart baselines.