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Dataset results
14 results for “MICCAI Challenges”
MICCAI 2016 MS lesion segmentation challenge: supplementary results
<p>This package contains supplementary material for our article prepared for publication and under revision. It contains omitted results due to space limits of the article as well as detailed, patient per patient and team per team results for all metrics. Additional figures redundant with those of the article are also provided. </p> <p>The readme file Readme_SupplementalMaterial.txt provides details about each individual file content.</p>
MItosis DOmain Generalization Challenge 2022 (MICCAI MIDOG 2022), Training data set (PNG version)
<p>This is the training dataset of the MItosis DOmain Generalization (MIDOG) challenge 2022, held in conjunction with MICCAI 2022. Please find the structured challenge description at 10.5281/zenodo.6362337.</p> <p>The training set consists of 405 tumor cases in total across six tumor types:</p> <ul> <li>Canine Lung Cancer (44 cases, scanned with 3DHistech Pannoramic Scan II)</li> <li>Human Breast Cancer (150 cases, scanned using three scanners, part of MIDOG2021 dataset)</li> <li>Canine Lymphoma (55 cases, scanned with 3DHistech Pannoramic Scan II)</li> <li>Human neuroendocrine tumor (55 cases, scanned with Hamamatsu NanoZoomer XR)</li> <li>Canine Cutaneous Mast Cell Tumor (50 cases, scanned with Aperio ScanScope CS2)</li> <li>Human melanoma (51 cases, scanned with Hamamatsu NanoZoomer XR) (no labels provided)</li> </ul> <p>From each WSI, a trained pathologist selected an area of 2mm² corresponding to approximately 10 high power fields, according to the grading scheme of Elston and Ellis. We cropped this area and provide it as PNG files in this data set due to restrictions in data set size on zenodo. Each file includes the resolution (in dots per inch, DPI) of the original scanned images.</p> <p>The training set contains 9501 mitotic figures (MF) and 11051 hard examples (non-mitotic figures). All annotations are provided in MS COCO JSON format and as SQLITE database (SlideRunner format).</p>
Dataset TrainBatch1 for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)
<p>Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)</p> <p>This is the TrainBatch1. </p> <p>We provide the casename correspondence information in the TrainBatch1_CaseInfo.csv file. It is convinient for researchers to locate the original cases in LIDC-IDRI with our provided full airway annotation for other research purposes.</p> <p>If you use this dataset in your research, you must cite the papers in the <strong>References </strong>below !!!</p>
Dataset Validation Images for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)
<p>Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)</p> <p>This is the Validation Image part. </p> <p>If you use this dataset in your research, you must cite the papers in the References below !!!</p>
Dataset TrainBatch2 for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)
<p>[Attention]: ATM_164_0000.nii.gz has misaligned label to CT image, please discard this case!</p> <p>Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)</p> <p>This is the TrainBatch2. </p> <p>If you use this dataset in your research, you must cite the papers in the References below !!!</p>
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
<p>Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high- quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to the risk posed to patients as a result of radiation dosage. To overcome these limitations, several studies have aimed to develop a no-reference novel image quality metric that correlates well with radiologists’ opinion on image quality without any reference images (2, 4, 5).</p> <p>Nevertheless, due to the lack of open-source datasets specifically for CT IQA, experiments have been conducted with datasets that differ from each other, rendering their results incomparable and introducing difficulties in determining a standard image quality metric for CT imaging. Besides, unlike real low-dose CT images with quality degradation due to various combinations of artifacts, most studies are conducted with only one type of artifact (e.g., low-dose noise (6-11), view aliasing (12), metal artifacts (13), scattering (14-16), motion artifacts (17-22), etc.). Therefore, this challenge aims to 1) evaluate various NR-IQA models on CT images containing complex noise/artifacts, 2) to compare their correlations with scores produced by radiologists, and 3) to grant insights into the determination of the best-performing metric of CT imaging in terms of correlating with the perception of radiologists’.</p> <p>Furthermore, considering that low-dose CT images are achieved by reducing the number of projections per rotation and by reducing the X-ray current, the combination of two major artifacts, namely the sparse view streak and noise generated by these methods, is dealt with in this challenge so that the best-performing IQA model applicable in real clinical environments can be verified.</p> <p> </p> <p><strong>Funding Declaration:</strong></p> <p>This research was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)), and by the National Research Foundation of Korea (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).</p> <p> </p> <p><strong>References:</strong></p> <ol> <li>Lee W, Cho E, Kim W, Choi J-H. Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment: SPIE, 2022.</li> <li>Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H. No-reference perceptual CT image quality assessment based on a self-supervised learning framework. Machine Learning: Science and Technology 2022.</li> <li>Choi D, Kim W, Lee J, Han M, Baek J, Choi J-H. Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 2021;32(116):1-14.</li> <li>Pal D, Patel B, Wang A. SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): IEEE, 2021; p. 1962-1965.</li> <li>Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 2012;21(12):4695-4708. doi: 10.1109/TIP.2012.2214050</li> <li>Lee J-YK, Wonjin; Lee, Yebin; Lee, Ji-Yeon; Ko, Eunji; Choi, Jang-Hwan. Unsupervised Domain Adaptation for Low-dose Computed Tomography Denoising. IEEE Access 2022.</li> <li>Jeon S-Y, Kim W, Choi J-H. MM-Net: Multi-frame and Multi-mask-based Unsupervised Deep Denoising for Low-dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2022.</li> <li>Kim W, Lee J, Kang M, Kim JS, Choi J-H. Wavelet subband-specific learning for low-dose computed tomography denoising. PloS one 2022;17(9):e0274308.</li> <li>Han M, Shim H, Baek J. Low-dose CT denoising via convolutional neural network with an observer loss function. Med Phys 2021;48(10):5727-5742. doi: 10.1002/mp.15161</li> <li>Kim B, Shim H, Baek J. Weakly-supervised progressive denoising with unpaired CT images. Med Image Anal 2021;71:102065. doi: 10.1016/j.media.2021.102065</li> <li>Wagner F, Thies M, Gu M, Huang Y, Pechmann S, Patwari M, Ploner S, Aust O, Uderhardt S, Schett G, Christiansen S, Maier A. Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography. Med Phys 2022;49(8):5107-5120. doi: 10.1002/mp.15718</li> <li>Kim B, Shim H, Baek J. A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation. Med Phys 2022. doi: 10.1002/mp.15885</li> <li>Kim S, Ahn J, Kim B, Kim C, Baek J. Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme. Med Phys 2022;49(9):6253-6277. doi: 10.1002/mp.15884</li> <li>Bier B, Berger M, Maier A, Kachelrieß M, Ritschl L, Müller K, Choi JH, Fahrig R. Scatter correction using a primary modulator on a clinical angiography Carm CT system. Med Phys 2017;44(9):e125-e137.</li> <li>Maul N, Roser P, Birkhold A, Kowarschik M, Zhong X, Strobel N, Maier A. Learning-based occupational x-ray scatter estimation. Phys Med Biol 2022;67(7). doi: 10.1088/1361-6560/ac58dc</li> <li>Roser P, Birkhold A, Preuhs A, Syben C, Felsner L, Hoppe E, Strobel N, Kowarschik M, Fahrig R, Maier A. X-Ray Scatter Estimation Using Deep Splines. IEEE Trans Med Imaging 2021;40(9):2272-2283. doi: 10.1109/TMI.2021.3074712</li> <li>Maier J, Nitschke M, Choi JH, Gold G, Fahrig R, Eskofier BM, Maier A. Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements. IEEE Trans Biomed Eng 2022;69(5):1608-1619. doi: 10.1109/TBME.2021.3123673</li> <li>Choi JH, Maier A, Keil A, Pal S, McWalter EJ, Beaupré GS, Gold GE, Fahrig R. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. II. Experiment. Med Phys 2014;41(6Part1):061902.</li> <li>Choi JH, Fahrig R, Keil A, Besier TF, Pal S, McWalter EJ, Beaupré GS, Maier A. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. Part I. Numerical modelbased optimization. Med Phys 2013;40(9):091905.</li> <li>Berger M, Muller K, Aichert A, Unberath M, Thies J, Choi JH, Fahrig R, Maier A. Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med Phys 2016;43(3):1235-1248. doi: 10.1118/1.4941012</li> <li>Ko Y, Moon S, Baek J, Shim H. Rigid and non-rigid motion artifact reduction in X-ray CT using attention module. Med Image Anal 2021;67:101883. doi: 10.1016/j.media.2020.101883</li> <li>Preuhs A, Manhart M, Roser P, Hoppe E, Huang Y, Psychogios M, Kowarschik M, Maier A. Appearance Learning for Image-Based Motion Estimation in Tomography. IEEE Trans Med Imaging 2020;39(11):3667-3678. doi: 10.1109/TMI.2020.3002695</li> </ol>
MICCAI FLARE22 Challenge Dataset (50 Labeled Abdomen CT Scans)
<p>This dataset was used as the labeled training set in MICCAI FLARE 2022 Challenge https://flare22.grand-challenge.org/.</p> <p>The CT images and pancreas annotations are from http://medicaldecathlon.com/</p> <p>The other organ annotations are from AbdomenCT-1K (research purpose only).</p> <p>If this dataset is useful in your research, please give credit to the following two papers:</p> <p>A large annotated medical image dataset for the development and evaluation of segmentation algorithms</p> <p>https://arxiv.org/abs/1902.09063</p> <p>AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?</p> <p>https://ieeexplore.ieee.org/document/9497733</p>
MICCAI 2021 MSSEG-2 challenge quantitative results
<p>This dataset includes all quantitative results of all metrics for challengers of the MICCAI MSSEG2 2021 challenge on new MS lesions detection. It also includes the ranking excel file obtained on the challenge day.</p> <p>Please refer to the README for more details.</p> <p>Second version adds missing SNAC team results and ITU team results update.</p> <p>Third version provides corrected results after correction of ground truth.</p>
MICCAI 2021 MSSEG-2 challenge demographics data
<p>This dataset will include all demographics and dataset constitution information for the MICCAI 2021 challenge.</p>
MICCAI 2016 challenge dataset demographics data
<p>This dataset contains supplementary material for the 2016 MS segmentation challenge data article. It contains the full demographic data for the datasets opened to the public.</p>
PROMISE12: Data from the MICCAI Grand Challenge: Prostate MR Image Segmentation 2012
<p>This repository contains all data associated with the 'Prostate MR Image Segmentation'-challenge 2012 on https://promise12.grand-challenge.org/. The goal of this challenge was to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate. </p> <p> </p>
Fetal Tissue Annotation Challenge (FeTA) Biometry - MICCAI 2024
<p>This dataset is the training dataset for the biometry task of FeTA Challenge held at the MICCAI 2024 Conference (Task 2). This derived dataset contains biometric measurement masks of five anatomical regions done on T2-weighted fetal brain super-resolution reconstructions: </p> <p>0. Non-relevant</p> <p>1. Height of the vermis</p> <p>2. Length of the corpus callosum</p> <p>3. Brain biparietal diameter</p> <p>4. Skull biparietal diameter</p> <p>5. Tranverse cerebellar diameter</p> <p>The data are released along a transform file that can be used to re-align the reconstructed T2w images into the plane that was used to execute the biometry.</p> <p>The goal of this dataset is to encourage research groups to develop automated biometry methods that are robust across a range of gestational agesm, multiple centers, a variety of brain pathologies as well as normally developing fetal brains. </p> <p>See fetachallenge.github.io for more details, and to register for the FeTA Challenge. Read carefully the README.md file for further details.</p> <p>If you use this dataset, please cite this repository 10.5281/zenodo.11192452.</p>
Training dataset for Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022
<p>Training dataset for <strong>Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022</strong>. </p> <p>Please refer to our website: <strong>https://vessel-wall-segmentation-2022.grand-challenge.org/</strong>.</p>
Validation dataset and reference code for Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022.
<p>Validation dataset and reference code for <strong>Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022</strong>. </p> <p>Please refer to our website: <strong>https://vessel-wall-segmentation-2022.grand-challenge.org/</strong>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.