Rethinking automatic segmentation of gross target volume from a decoupling perspective.

Accurate and reliable segmentation of Gross Target Volume (GTV) is critical in cancer Radiation Therapy (RT) planning, but manual delineation is time-consuming and subject to inter-observer variations. Recently, deep learning methods have achieved remarkable success in medical image segmentation. However, due to the low image contrast and extreme pixel imbalance between GTV and adjacent tissues, most existing methods usually obtained limited performance on automatic GTV segmentation.

FcTC-UNet: Fine-grained Combination of Transformer and CNN for Thoracic Organs Segmentation.

Precise segmentation of organs at risk (OARs) in computed tomography (CT) images is an essential step for lung cancer radiotherapy. However, the manual delineation of OARs is time-consuming and subject to inter-observer variation. Although U-like architecture has achieved great success in medical image segmentation recently, it exhibits the limitations in modeling long-range dependencies.

Automatic Segmentation of Target Structures for Total Marrow and Lymphoid Irradiation in Bone Marrow Transplantation.

The use of total marrow and lymphoid irradiation (TMLI) as part of conditioning regimens for bone marrow transplantation is trending due to its advantages in disease control and low toxicity. Accurate contouring of target structures such as bone and lymph nodes plays an important role in irradiation planning. However, this process is often time-consuming and prone to inter-observer variation.

Whole Slide Image Multi-Classification of Cervical Epithelial Lesions Based on Unsupervised Pre-training.

Cervical cancer has become one of the important factors threatening women's health. Histopathological diagnosis is the most important criterion for cervical cancer diagnosis and treatment. Accurate classification of lesion degree of cervical epithelium by analyzing whole slide images (WSIs) can effectively improve the therapeutic effect and prognosis.

ITUnet: Integration Of Transformers And Unet For Organs-At-Risk Segmentation.

Recently, convolutional neural network(CNN) has achieved great success in medical image segmentation. However, due to the limitation of convolutional receptive field, the pure convolutional neural network is difficult to further improve its performance. Given the outstanding ability of transformers in extracting the long-range dependency, some works have successfully applied it to computer vision and achieved better results than CNN in some tasks.