Segmentation of robotic instruments is an important problem for robotic assisted minimially invasive surgery. In this challenge we invite applicants to participate in 3 different tasks: binary segmentation, multi-label segmentation and instrument recognition. Binary segmentation involves just separating the image into instruments and background, whereas multi-label segmentation requires the user to also recognize which parts of the instrument body correspond The final recogition task tests whether the user can recognize which segmentation corresponds to which da Vinci instrument type. Description from: Robotic Instrument Segmentation Sub-Challenge
21 PAPERS • 2 BENCHMARKS
The Segmentation of Underwater IMagery (SUIM) dataset contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins
26 PAPERS • 2 BENCHMARKS
…There are two major challenges to allowing such an attractive learning modality for segmentation tasks: i) a large-scale benchmark for assessing algorithms is missing; ii) unsupervised shape representation We propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to track the research progress. Based on the ImageNet dataset, we propose the ImageNet-S dataset with 1.2 million training images and 50k high-quality semantic segmentation annotations for evaluation.
31 PAPERS • 6 BENCHMARKS
The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding The 2021 Kidney and Kidney Tumor Segmentation Challenge The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
7 PAPERS • 1 BENCHMARK
ScribbleKITTI is a scribble-annotated dataset for LiDAR semantic segmentation.
16 PAPERS • 2 BENCHMARKS
The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels.
999 PAPERS • 25 BENCHMARKS
SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation.
540 PAPERS • 10 BENCHMARKS
…WoodScape comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection.
49 PAPERS • 1 BENCHMARK