Automated Skeletal Bone Age Assessment with Two-Stage Convolutional Transformer Network Based on X-ray Images.

Human skeletal development is continuous and staged, and different stages have various morphological characteristics. Therefore, bone age assessment (BAA) can accurately reflect the individual's growth and development level and maturity. Clinical BAA is time consuming, highly subjective, and lacks consistency.

Automatic volumetric diagnosis of hepatocellular carcinoma based on four-phase CT scans with minimum extra information.

Summary: We built a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI, demonstrating high performance and excellent efficiency.

Objectives: The aim of this study was to develop a deep-learning-based model for the diagnosis of hepatocellular carcinoma.

Materials And Methods: This clinical retrospective study uses CT scans of liver tumors over four phases (non-enhanced phase, arterial phase, portal venous phase, and delayed phase).

DeepRecS: From RECIST Diameters to Precise Liver Tumor Segmentation.

Liver tumor segmentation (LiTS) is of primary importance in diagnosis and treatment of hepatocellular carcinoma. Known automated LiTS methods could not yield satisfactory results for clinical use since they were hard to model flexible tumor shapes and locations. In clinical practice, radiologists usually estimate tumor shape and size by a Response Evaluation Criteria in Solid Tumor (RECIST) mark.

PA-ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images.

Purpose: Liver tumor segmentation is a crucial prerequisite for computer-aided diagnosis of liver tumors. In the clinical diagnosis of liver tumors, radiologists usually examine multiphase CT images as these images provide abundant and complementary information of tumors. However, most known automatic segmentation methods extract tumor features from CT images merely of a single phase, in which valuable multiphase information is ignored.

Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function.

Purpose: Automatic liver segmentation from abdominal computed tomography (CT) images is a fundamental task in computer-assisted liver surgery programs. Many liver segmentation algorithms are very sensitive to fuzzy boundaries and heterogeneous pathologies, especially when the data are scarce. To solve these problems, we propose an automatic liver segmentation framework based on three-dimensional (3D) convolutional neural networks with a hybrid loss function.

Multiple extra-articular synovial cysts accompanied by rheumatoid arthritis in the bilateral elbow joints: A case report.

Rationale: Synovial cysts are well known in rheumatoid arthritis (RA), and most common in the popliteal fossa. They may produce lots of local symptoms and complaints, which may present initially as an unrelated clinical condition. Few studies have reported multiple extra-articular synovial cysts (MESCs) in the RA patients.