Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

3D semi-supervised medical image segmentation is extremely essential in computer-aided
diagnosis, which can reduce the time-consuming task of performing annotation. The …

A network based on complementary consistency training, CC-Net, has been proposed for
semi-supervised left atrium image segmentation. CC-Net efficiently utilizes unlabeled data …

Semi-supervised learning is becoming an effective solution in medical image segmentation
because annotations are costly and tedious to acquire. Methods based on the teacher …

3D medical image segmentation methods have been successful, but their dependence on
large amounts of voxel-level annotated data is a disadvantage that needs to be addressed …

Automatic segmentation of vertebral bodies (VBs) and intervertebral discs (IVDs) in 3D
magnetic resonance (MR) images is vital in diagnosing and treating spinal diseases …

Left atrial (LA) fibrosis plays a vital role as a mediator in the progression of atrial fibrillation.
3D late gadolinium-enhancement (LGE) MRI has been proven effective in identifying LA …

Deep learning has advanced the automation and intelligence levels of medical image
segmentation, but the acquisition of annotations for medical images proves to be very …

Background and objectives Deep learning-based methods have made significant progress
in the field of hyperspectral pathological image segmentation. However, these methods …

Semi-supervised image segmentation aims to train the neural network with a small number
of labeled images and a large number of unlabeled images, which helps to alleviate the …