A 3D hierarchical cross-modality interaction network using transformers and convolutions for brain glioma segmentation in MR images.

Background: Precise glioma segmentation from multi-parametric magnetic resonance (MR) images is essential for brain glioma diagnosis. However, due to the indistinct boundaries between tumor sub-regions and the heterogeneous appearances of gliomas in volumetric MR scans, designing a reliable and automated glioma segmentation method is still challenging. Although existing 3D Transformer-based or convolution-based segmentation networks have obtained promising results via multi-modal feature fusion strategies or contextual learning methods, they widely lack the capability of hierarchical interactions between different modalities and cannot effectively learn comprehensive feature representations related to all glioma sub-regions.

Prostate lesion segmentation based on a 3D end-to-end convolution neural network with deep multi-scale attention.

Prostate cancer is one of the deadest cancers among human beings. To better diagnose the prostate cancer, prostate lesion segmentation becomes a very important work, but its progress is very slow due to the prostate lesions small in size, irregular in shape, and blurred in contour. Therefore, automatic prostate lesion segmentation from mp-MRI is a great significant work and a challenging task.

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation.

Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations.