Computer Vision News - October 2021

Overview of UNETR. Our proposed model consists of a transformer encoder that directly utilizes 3D patches and is connected to a CNN-based decoder via skip connection. Qualitative comparison of different baselines. UNETR has a significantly better segmentation accuracy for left and right adrenal glands, and UNETR is the only model to correctly detect branches of the adrenal glands. UNETR proposes to use a patch-based approach with a transformer-based encoder to increase the model’s capability for learning long-range dependencies and effectively capturing global contextual representation at multiple scales. For instance, in the multi-organ segmentation task, UNETR can accurately segment organs with complex shapes (e.g. adrenal glands) and low contrast (e.g. portal veins) while CNN-based approaches fail to accurately segment these organs. See the figure above for more qualitative comparisons between UNETR and other CNN-based and transformer- based segmentation models. UNETR has shown promising performance on various volumetric medical image segmentation tasks such as multi-organ segmentation using Multi Atlas Labeling Beyond The 3 Summary Su mary 04 10 16 22 30 32 54 64 Removing Diffraction Image... Computer Vision Research by Marica Muffoletto Spleenlab AI Systems for Autonomous Mobility Computer Vision for Automated... ICCV Workshop Preview RePAIR Pompeii AI for Archeology Transformers in Medical Imaging by NVIDIA and MONAI Bladder Panorama Generator AI for Urology Creating a multi-object tracking... Coding Workshop by Ioannis Valasakis 54 32 10 64 16 04 30 22 Best of MICCAI The best features chosen by us Computer Vision News Medical Imaging News Best of MICCAI 2021