1. Introduction
Medical image segmentation is an important prerequisite of computer-aided diagnosis (CAD) which has been applied in a wide range of clinical applications. With the emerging of deep learning, great achievements have been made in this area. However, it remains very difficult to get satisfying segmentation for some challenging structures, which could be extremely small with respect to the whole volume, or vary a lot in terms of location, shape, and appearance. Besides, abnormalities, which results in a huge change in texture, and anisotropic property (different voxel spacing) make the segmentation tasks even harder. Some examples are showed in Fig 1.
Image and mask examples from MSD tasks (from left to right and top to bottom): Brain tumours, lung tumours, hippocampus, hepatic vessel and tumours, pancreas tumours, and liver tumours, respectively. The abnormalities, texture variance, and anisotropic properties make it very challenging to achieve satisfying segmentation performance. Red, green, and blue correspond to labels 1, 2 and 3, respectively, of each dataset.