In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep
learning algorithms require lots of voxel-wise annotations, which are usually difficult …

We propose a novel teacher-student model for semi-supervised multi-organ segmentation.
In the teacher-student model, data augmentation is usually adopted on unlabeled data to …

Accurate multi-organ abdominal CT segmentation is essential to many clinical applications
such as computer-aided intervention. As data annotation requires massive human labor …

Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …

Recent trends in semi-supervised learning have significantly boosted the performance of 3D
semi-supervised medical image segmentation. Compared with 2D images, 3D medical …

Whole abdominal organ segmentation is important in diagnosing abdomen lesions,
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …

Pretraining with large-scale 3D volumes has a potential for improving the segmentation
performance on a target medical image dataset where the training images and annotations …

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical
applications such as computer-aided diagnosis and computer-aided surgery. But this task is …

It has been widely recognized that the success of deep learning in image segmentation
relies overwhelmingly on a myriad amount of densely annotated training data, which …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …