Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning

MR images of knee joint are usually collected in axial, coronal, and sagittal views with large slice spacing for clinical study. Current methods either segment images in different views separately or apply super-resolution fusion before 3D segmentation. Knee images segmentation transfer between different views is still an open problem. Moreover, the majority of manual labelling works focus on the sagittal-view, and practically it is hard to collect label maps for the coronal- and axial-views, which are also invaluable for observing knee injuries. In this paper, we propose a novel algorithm to transfer sagittal-view annotations to the other views. First, we build a supervised low-resolution segmentation (LR-Seg) module based on the down-sampled sagittal-view slices to obtain the label map on the target view. And then a context transfer module is proposed to refine the segmentations using target-view context. Then by iterative learning of these two modules, the context from one result can be used to guide the training of the other. Experimental results show that our algorithm can greatly alleviate the burden of manually labeling works from clinicians and gain comparable segmentation results on axial and coronal views.