Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data

ABCs - Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images.- Cross-modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization.- Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread.- Ensembled ResUnet for Anatomical Brain Barriers Segmentation.- An Enhanced Coarse-to-_ne Framework for the segmentation of clinical target volume.- Automatic Segmentation of brain structures for treatment planning optimization and target volume definition.- A Bi-Directional, Multi-Modality Framework for Segmentation of Brain Structures.- L2R - Learn2Reg: Multitask and Multimodal 3D Medical Image Registration.- Large Deformation Image Registration with Anatomy-aware Laplacian Pyramid Networks.- Discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge.- Variable Fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg Challenge.- Learning a deformable registration pyramid.- Deep learning based registration using spatial gradients and noisy segmentation labels.- Multi-step, Learning-based, Semi-supervised Image Registration Algorithm.- Using Elastix to register inhale/exhale intrasubject thorax CT: a unsupervised baseline to the task 2 of the Learn2Reg challenge.- TN-SCUI - Thyroid Nodule Segmentation and Classification in Ultrasound Images.- Cascade Unet and CH-Unet for thyroid nodule segmenation and benign and malignant classification.- Identifying Thyroid Nodules in Ultrasound Images through Segmentation-guided Discriminative Localization.- Cascaded Networks for Thyroid Nodule Diagnosis from Ultrasound Images.- Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks.- LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images.- Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation.