Accurate Tumor Segmentation via Octave Convolution Neural Network.

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks.

Brain Tumor Segmentation via Multi-Modalities Interactive Feature Learning.

Automatic segmentation of brain tumors from multi-modalities magnetic resonance image data has the potential to enable preoperative planning and intraoperative volume measurement. Recent advances in deep convolutional neural network technology have opened up an opportunity to achieve end-to-end segmenting the brain tumor areas. However, the medical image data used in brain tumor segmentation are relatively scarce and the appearance of brain tumors is varied, so that it is difficult to find a learnable pattern to directly describe tumor regions.

COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations.

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019. Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections.