Minecraft Segmentation is a segmentation dataset for the Minecraft House that adds semantic segmentation labels for sub-components of the house.
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…Nevertheless, this means, that an instance segmentation of all components and objects of interest into disjoint entities from the CT data is necessary. As of currently, no adequate computer-assisted tools for automated or semi-automated segmentation of such XXL-airplane data are available, in a first step, an interactive data annotation and object labelling
Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. The tasks evaluated for include: vertebral labelling and segmentation.
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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
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…In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. We map all moving-x classes of the original SemanticKITTI semantic segmentation benchmark to a single moving object class. Citation Citation. More information on the task and the metric, you can find in our publication related to the task: @article{chen2021ral, title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach
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…We recommend to use Multi Atlas Segmentation and Morphometric analysis toolkit (MASMAT) for mouse brain MRI along with other mouse brain atlases in this repo.
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DOORS is a dataset designed for boulders recognition, centroid regression, segmentation, and navigation applications. It can be used to perform navigation, boulder recognition, segmentation, and centroid regression. Segmentation: Contain images, masks, and labels of 2 datasets: DS1 and DS2. DS1 is made of the same images of the Regression dataset but is specifically designed for segmentation.
The goal of the challenge is to compare automated algorithms that are able to detect and segment various types of fluids on a common dataset of optical coherence tomography (OCT) volumes representing different We invite the medical imaging community to participate by developing and testing existing and novel automated retinal OCT segmentation methods.
…A precise three-dimensional spatial description, i.e. segmentation, of the target volumes as well as OARs is required for optimal radiation dose distribution calculation, which is primarily performed using Although attempts have been made towards the segmentation of OARs from MR images, so far there has been no evaluation of the impact the combined analysis of CT and MR images has on the segmentation of The Head and Neck Organ-at-Risk Multi-Modal Segmentation Challenge aims to promote the development of new and application of existing fully automated techniques for OAR segmentation in the HaN region from CT images that exploit the information of multiple imaging modalities so as to improve the accuracy of segmentation results.
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The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent
The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and
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…OCTScenes-A dataset, the 0--3099 scenes without segmentation annotation are for training, while the 3100--3199 scenes with segmentation annotation can be used for testing. In the OCTScenes-B dataset, the 0--4899 scenes without segmentation annotation are for training, while the 4900--4999 scenes with segmentation annotation can be used for testing.
…Each reconstruction has clean dense geometry, high resolution and high dynamic range textures, glass and mirror surface information, planar segmentation as well as semantic class and instance segmentation
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The ScanNet200 benchmark studies 200-class 3D semantic segmentation - an order of magnitude more class categories than previous 3D scene understanding benchmarks. The source of scene data is identical to ScanNet, but parses a larger vocabulary for semantic and instance segmentation
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Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance evaluation via segmentation challenges
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The Waymo Open Dataset currently contains 1,950 segments. The authors plan to grow this dataset in the future. Currently the datasets includes: 1,950 segments of 20s each, collected at 10Hz (390,000 frames) in diverse geographies and conditions Sensor data 1 mid-range lidar 4 short-range lidars 5 cameras (front data Lidar to camera projections Sensor calibrations and vehicle poses Labeled data Labels for 4 object classes - Vehicles, Pedestrians, Cyclists, Signs High-quality labels for lidar data in 1,200 segments 12.6M 3D bounding box labels with tracking IDs on lidar data High-quality labels for camera data in 1,000 segments 11.8M 2D bounding box labels with tracking IDs on camera data
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Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset.
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…Usage: 2D/3D image segmentation Format: HDF5 Libraries to read HDF5 files: 1) silx: https://github.com/silx-kit/silx 2) h5py: https://www.h5py.org 3) pymicro: https://github.com/heprom/pymicro Trained models to segment this dataset: https://doi.org/10.5281/zenodo.4601560 Please cite us as @ARTICLE{10.3389/fmats.2021.761229, AUTHOR={Bertoldo, João P. C. and Decencière, Etienne and Ryckelynck, David and Proudhon, Henry}, TITLE={A Modular U-Net for Automated Segmentation of X-Ray Tomography Images in Composite Materials}, JOURNAL={Frontiers in
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1、 Competition name: The 2nd China Society of Image and Graphics (CSIG) Image and Graphics Technology Challenge: MRSpineSeg Challenge: Automated Multi-class Segmentation of Spinal Structures on Volumetric This competition aims to gather global developers to explore efficient and accurate 3D automatic segmentation of spinal structure in MR images by using artificial intelligence technology. The spinal structure to be segmented includes 10 vertebrae and 9 intervertebral discs. 3、 Organizer: Qianjin,Feng, School of Biomedical Engineering, Southern Medical University, Guangdong Key Laboratory SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation [J]. DGMSNet: Spine Segmentation for MR Image by a Detection-Guided Mixed-supervised Segmentation Network [J]. Medical Image Analysis, 2022, 102261.
The SemanticPOSS dataset for 3D semantic segmentation contains 2988 various and complicated LiDAR scans with large quantity of dynamic instances.
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…This dataset includes the scans, registrations to the SMPL model, scans segmented in clothing parts, garment category and size labels.
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…The framework used for the annotation process draws on crowdsourcing to segment surfaces from photos, and then annotate them with rich surface properties, including material, texture and contextual information
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The MM-WHS 2017 dataset is a dataset for multi-modality whole heart segmentation. It provides 20 labeled and 40 unlabeled CT volumes, as well as 20 labeled and 40 unlabeled MR volumes.
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…The images are labeled with different types of annotations such as segmentation labels, pose or 3D.
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…Each frame has a semantic segmentation of the objects in the scene and information about the camera pose. It is composed by 415 sequences captured in 254 different spaces, in 41 different buildings.
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…Each scene is a residential building consisting of multiple rooms and floor levels, and is annotated with surface construction, camera poses, and semantic segmentation.
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…The comparison is performed not only by evaluating the quality of neuron segmentations, but also by assessing the accuracy of detecting synapses and identifying synaptic partners. The challenge is carried out on three large and diverse datasets from adult Drosophila melanogaster brain tissue, comprising neuron segmentation ground truth and annotations for synaptic connections.
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…The dataset provides valuable data for learning how people design, including sequential CAD design data, designs segmented by modelling operation, and design hierarchy and connectivity data.
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…Leaf wood labels were transferred from contemporaneous (2021) TLS acquisition, for which segmentation was done using LeWoS and onscreen post correction.
…Two sets: ModelNet-C for point cloud classification and ShapeNet-C for part segmentation. Real-world corruption sources, ranging from object-, senor-, and processing-levels.
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…Each sample provides: RGB image (320x320 pixels) Depth map (320x320 pixels) Segmentation masks (320x320 pixels) for the classes: background, person, three classes for each finger and one for each palm
…Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction.
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…AGORA provides (1) SMPL/SMPL-X parameters and (2) segmentation masks for each subject in images.
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The platelet-em dataset contains two 3D scanning electron microscope (EM) images of human platelets, as well as instance and semantic segmentations of those two image volumes.
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…For each image, the dataset contains the 3D poses, per-pixel class segmentation, and 2D/3D bounding box coordinates for all objects.
Automated leaf segmentation is a challenging area in computer vision.
…The benchmark is based on more than 250K high-resolution video frames, all annotated with ground-truth data for both low-level and high-level vision tasks, including optical flow, semantic instance segmentation
…This outdoor dataset introduces falling_snow and accumulated_snow along with all the semanticKITTI classes to further AV tasks like semantic and panoptic segmentation, object detection and tracking, and
A dataset of 100K synthetic images of skin lesions, ground-truth (GT) segmentations of lesions and healthy skin, GT segmentations of seven body parts (head, torso, hips, legs, feet, arms and hands), and
…Tasks our dataset support: Generaliazable Novel view synthesis (Few shot evaluation) Novel view synthesis (Overfitting evaluation) 6D pose estimation Object editing Depth estimation Semantic Segmentation Instance Segmentation
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…Three human raters segmented the resection cavity on partially overlapping subsets of EPISURG: Rater 1: 133 subjects (researcher in neuroimaging) Rater 2: 34 subjects (clinical research fellow) Rater dataset for your research please cite the following publications: Pérez-García F., Rodionov R., Alim-Marvasti A., Sparks R., Duncan J.S., Ourselin S. (2020) Simulation of Brain Resection for Cavity Segmentation
…The images in the dataset are formatted according to the following protocol: CASE NUMBER | FILE TYPE (LOC OR SEG) | FILE EXTENSION | where loc is the original image and seg is the associated segmentation
…Squats Bird Dogs Supermans Bicycle Crunches Leg Raises Front Raises (with dumbbells) Overhead Press (with dumbbells) Annotations The dataset includes the following annotations: Bounding boxes Segmentation
Our project (STPLS3D) aims to provide a large-scale aerial photogrammetry dataset with synthetic and real annotated 3D point clouds for semantic and instance segmentation tasks.
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…Atlas was created based on the original MRM NeAt mouse brain atlas (template images reoriented and bias-corrected, left/right structure label seperated, and 4th ventricle manual segmentation added). Citation If you use the segmented brain structure, or use the atlas along with the automatic mouse brain MRI segmentation tools, we ask you to kindly cite the following papers: Ma D, Cardoso MJ, Modat
…modern, learning-based techniques for a variety of material-related tasks including, but not limited to, material acquisition, material generation and synthetic data generation e.g. for retrieval or segmentation