…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.
2 PAPERS • NO BENCHMARKS YET
CHAOS challenge aims the segmentation of abdominal organs (liver, kidneys and spleen) from CT and MRI data. CHAOS tasks contain combination of these organs' segmentation. " [1] and it is simply based on using a single system, which can segment liver from both CT and MRI. is mostly a regular task of liver segmentation from CT, (such as SLIVER07). segmentation from MRI.
8 PAPERS • NO BENCHMARKS YET
…Segmentation, meant as the partition of MR brain images in multiple anatomical classes, is an essential step in many functional and structural neuroimaging studies. In this work, we design and test CEREBRUM-7T, an optimised end-to-end CNN architecture, that allows to segment a whole 7T T1w MRI brain volume at once, without the need of partitioning it into 2D or 3D The generated model is able to produce accurate multi-structure segmentation masks on six different classes, in only few seconds. In the experimental part, we show that the proposed solution outperforms the GT it was trained on in segmentation accuracy. For more details, please visit: https://rocknroll87q.github.io/cerebrum7t
1 PAPER • NO BENCHMARKS YET
…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.
5 PAPERS • NO BENCHMARKS YET
…2021 challenge utilizes multi-institutional pre-operative baseline multi-parametric magnetic resonance imaging (mpMRI) scans, and focuses on the evaluation of state-of-the-art methods for (Task 1) the segmentation
10 PAPERS • NO BENCHMARKS YET
BRATS 2014 is a brain tumor segmentation dataset.
5 PAPERS • 1 BENCHMARK
…The ACDC dataset contains cardiac MRI images, paired with hand-made segmentation masks. It is possible to use the segmentation masks provided in the ACDC dataset to evaluate the performance of methods trained using only scribble supervision. References: [1] Bernard, Olivier, et al. "Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?." IEEE transactions on medical imaging 37.11 (2018): 2514-2525.
9 PAPERS • 1 BENCHMARK
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
6 PAPERS • 1 BENCHMARK
…The segmentation evaluation is based on three tasks: WT, TC and ET segmentation.
71 PAPERS • 1 BENCHMARK
The BraTS 2015 dataset is a dataset for brain tumor image segmentation. It consists of 220 high grade gliomas (HGG) and 54 low grade gliomas (LGG) MRIs. Segmented “ground truth” is provide about four intra-tumoral classes, viz. edema, enhancing tumor, non-enhancing tumor, and necrosis.
65 PAPERS • 1 BENCHMARK
…This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient knee MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four Challenge Tracks DICOM Track: The DICOM benchmarking track uses scanner-generated DICOM images as the input for image segmentation and detection tasks. Raw Data Track: The Raw Data benchmarking track uses raw MRI data (i.e. k-space) as the input for image reconstruction, segmentation and detection tasks.
6 PAPERS • NO BENCHMARKS YET
BRATS 2016 is a brain tumor segmentation dataset. It shares the same training set as BRATS 2015, which consists of 220 HHG and 54 LGG. Its testing dataset consists of 191 cases with unknown grades.
13 PAPERS • NO BENCHMARKS YET
The PROMISE12 dataset was made available for the MICCAI 2012 prostate segmentation challenge.
72 PAPERS • 1 BENCHMARK
…This dataset contains 180 subjects preprocessed images, and each subject comprises a brain MR image and a brain CT image with corresponding segmentation label.
1 PAPER • 1 BENCHMARK
The Sunnybrook Cardiac Data (SCD), also known as the 2009 Cardiac MR Left Ventricle Segmentation Challenge data, consist of 45 cine-MRI images from a mixed of patients and pathologies: healthy, hypertrophy Subset of this data set was first used in the automated myocardium segmentation challenge from short-axis MRI, held by a MICCAI workshop in 2009.
BRATS 2013 is a brain tumor segmentation dataset consists of synthetic and real images, where each of them is further divided into high-grade gliomas (HG) and low-grade gliomas (LG).
35 PAPERS • 2 BENCHMARKS
…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
…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