Segmenting partially annotated medical images
Nicolas Martin, Jean–Pierre Chevallet, Georges Quénot · 2022
Segmentation of medical images using learning based systems remains a challenge in medical computer vision: training a segmentation model requires medical images exhaustively annotated by experts that are difficult and expensive to obtain. We propose to explore the usage of partially annotated images, i.e., all images are annotated but not all regions of a given class are annotated. In this paper, we propose several approaches and we experiment them on the segmentation of intra-oral images. First, we propose to modify the loss function to consider only the annotated areas, and second to integrate annotation from non-expert, as well as the combination of these methods. The experiments we conducted showed an improvement up to 33% on the segmentation performance. This approach allows to obtain better quality annotation masks than the initial human annotation using only partially annotated areas or non-expert annotations. In the future, these approaches can be extended by combination with active learning methods.