Multi-task Shape Regression for Medical Image Segmentation

Abstract

In this paper, we propose a general segmentation framework of Multi-Task Shape Regression (MTSR) which formulates segmentation as multi-task learning to leverage its strength of jointly solving multiple tasks enhanced by capturing task correlations. The MTSR entirely estimates coordinates of all points on shape contours by multi-task regression, where estimation of each coordinate corresponds to a regression task; the MTSR can jointly handle nonlinear relationships between image appearance and shapes while capturing holistic shape information by encoding coordinate correlations, which enables estimation of highly variable shapes, even with vague edge or region inhomogeneity. The MTSR achieves a long-desired general framework without relying on any specific assumptions or initialization, which enables flexible and fully automatic segmentation of multiple objects simultaneously, for different applications irrespective of modalities. The MTSR is validated on six representative applications of diverse images, achieves consistently high performance with dice similarity coefficient (DSC) up to 0.93 and largely outperforms state of the arts in each application, which demonstrates its effectiveness and generality for medical image segmentation.