Single nucleus segmentation is a frequent challenge of microscopy image processing, since
it is the first step of many quantitative data analysis pipelines. The quality of tracking single …

Oriented object detection in aerial images is a challenging task as the objects in aerial
images are displayed in arbitrary directions and are usually densely packed. Current …

The instance segmentation of cell images is the basis for conducting cell research and is of
great importance for the study and diagnosis of pathologies. To analyze current situations …

Segmentation is a fundamental process in microscopic cell image analysis. With the advent
of recent advances in deep learning, more accurate and high-throughput cell segmentation …

Reducing the complexity of the pipeline of instance segmentation is crucial for real-world
applications. This work addresses this issue by introducing an anchor-box free and single …

Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases.
Despite the recent success of deep learning-based cell segmentation methods, it remains …

Separating and labeling each nuclear instance (instance-aware segmentation) is the key
challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been …

Hispathological image segmentation algorithms play a critical role in computer aided
diagnosis technology. The development of weakly supervised segmentation algorithm …

The human immune system consists of a highly intelligent network of billions of
independent, self-organized cells that interact with each other. Machine learning (ML) is an …

Adolescent idiopathic scoliosis (AIS) is a lifetime disease that arises in children. Accurate
estimation of Cobb angles of the scoliosis is essential for clinicians to make diagnosis and …