CabiNet: A Deep Learning Framework for Multiclass Medical Image Segmentation from Multiple Single Class Datasets

Abstract

The development of deep learning based image segmentation algorithms is often hindered by the lack of adequately annotated datasets, and this issue becomes a severe bottleneck in multi-class segmentation. The models can learn binary image segmentation since the datasets are often labeled for a single class. However, the task becomes more challenging for multi-class annotations and segmentation of regions of interest, e.g., multi-organ segmentation that plays a vital role in computer-assisted diagnosis, surgery planning, and other related applications. This study proposes a framework, CabiNet, that attempts to solve data bottleneck by learning multi-class healthy organ segmentation from partially annotated abdominal MRI data, i.e., multiple single-class datasets. Multiple organ specific expert networks and a Jack of All (JoA) network are trained to generate posterior probability maps of individual organs simultaneously which are accumulated to get the final posterior probability distributions over different organs for each pixel. On benchmark Combined Healthy Abdominal Organ Segmentation (CHAOS) MRI dataset CabiNet yielded very promising dice scores: liver (90.39%), right kidney (87.41%), left kidney (81.09%), spleen (90.78%) and overall average dice score as 87.4%.