3D Medical Imaging Segmentation

Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource. However, a large-scale, public dataset is currently unavailable. VerSe is a large scale, multi-detector, multi-site, CT spine dataset consisting of 374 scans from 355 patients. The challenge was held in two iterations in conjunction with MICCAI 2019 and 2020. The tasks evaluated for include: vertebral labelling and segmentation. Image source: https://arxiv.org/pdf/2001.09193v5.pdf

The SKM-TEA dataset pairs raw quantitative knee MRI (qMRI) data, image data, and dense labels of tissues and pathology for end-to-end exploration and evaluation of the MR imaging pipeline. 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 tissues, and bounding box annotations for sixteen clinically relevant pathologies. ## 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.

A Benchmark for Robust Multi-Hop Spatial Reasoning in Texts

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. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. 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 hidden, n=316) data. 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. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research. Official Paper: https://www.nature.com/articles/s41597-022-01401-7

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 anatomy. 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

The RAD-ChestCT dataset is a large medical imaging dataset developed by Duke MD/PhD Rachel Draelos during her Computer Science PhD supervised by Lawrence Carin. The full dataset includes 35,747 chest CT scans from 19,661 adult patients. The public Zenodo repository contains an initial release of 3,630 chest CT scans, approximately 10% of the dataset. This dataset is of significant interest to the machine learning and medical imaging research communities.

Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-class segmentation problem. To address this, we develop the Brain Hemorrhage Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset containing 192 volumes with pixel-level annotations and 2200 volumes with slice-level annotations across five categories of ICH. To demonstrate the utility of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.

Cancer in the region of the head and neck (HaN) is one of the most prominent cancers, for which radiotherapy represents an important treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). 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 computed tomography (CT) images. However, the HaN region contains many OARs that are poorly visible in CT, but better visible in magnetic resonance (MR) images. 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 OARs in the HaN region. 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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EPISURG is a clinical dataset of $T_1$-weighted magnetic resonance images (MRI) from 430 epileptic patients who underwent resective brain surgery at the National Hospital of Neurology and Neurosurgery (Queen Square, London, United Kingdom) between 1990 and 2018. The NIfTI files are anonymised and the images have been defaced to further protect the patients' identity. The dataset comprises 430 postoperative MRI. The corresponding preoperative MRI is present for 269 subjects. 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 3: 33 subjects (neurologist) ## Acknowledgements If you use this 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 Using Self-supervised and Semi-supervised Learning. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture Notes in Computer Science, vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_12 Pérez-García F., Rodionov R., Alim-Marvasti A., Sparks R., Duncan J.S., Ourselin S. EPISURG: MRI dataset for quantitative analysis of resective neurosurgery for refractory epilepsy. University College London (2020). DOI 10.5522/04/9996158.v1 ## Graphical user interface (GUI) The 3D Slicer extension EPISURG may be used to visualise the dataset. ## Data use agreement The EPISURG data are distributed to the greater scientific community under the following terms: 1. You will not attempt to establish the identity or to make contact with any of the included subjects. 2. You will acknowledge the use of EPISURG data and data derived from EPISURG data when publicly presenting any results or algorithms that benefitted from their use. Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from EPISURG data should cite the publications listed above. 3. You will not further disclose these data beyond the uses outlined in this agreement and understand that redistribution of data in any manner is prohibited. 4. You will require anyone on your team who uses these data, or anyone with whom you share these data to comply with this data use agreement.

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 MR Images. 2、 Purpose： Degenerative spine diseases (e.g., lumbar disc herniation, spinal stenosis, etc.) have become important diseases affecting the health and quality of life of the elderly and working people. These degenerative spinal diseases often cause changes in the structural morphology and mechanical systems of the spine, resulting in pain, such as lumbar disc herniation, reduced disc height, and nerve compression. Magnetic resonance imaging (MRI) as a non-invasive examination method, it has good soft tissue imaging and no radiation. It is a reliable screening method for degenerative spine diseases. In clinical practice, the treatment of degenerative spinal disorders depends largely on physicians’ experience and lacks accurate quantitative analysis tools. 3D automatic segmentation (MR) images of multiclass spinal structures by MRI are a prerequisite for 3D reconstruction of spinal structures. It can provide quantitative analysis tools for building biomechanical models of the spine, simulating stresses in spinal structures, and assessing the prognosis of different treatment options for degenerative spinal diseases. 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 of medical image processing, China. 4、 Requirements for competition participants： It is open to the whole society. Personnel from colleges and universities, scientific research institutions and enterprises can sign up for the competition. The maximum number of each team is four. Each person can only participate in one team. After team registration, the team information cannot be changed. Note: all personnel who have access to the competition data are prohibited from participating in the competition. Those who have not access to the competition data of Southern Medical University can also participate in the competition. Southern Medical University has the right of final interpretation. 5、 Timeline: Registration (March 30, 2021 – May 22, 2021) 6、Citation: [1] Shumao Pang, Chunlan Pang, Lei Zhao, Yangfan Chen, Zhihai Su, Yujia Zhou, Meiyan Huang, Wei Yang, Hai Lu, Qianjin Feng*. SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation [J]. IEEE Transactions on Medical Imaging, 2021, 40(1): 262-273. [2] Shumao Pang, Chunlan Pang, Zhihai Su, Liyan Lin, Lei Zhao, Yangfan Chen, Yujia Zhou, Hai Lu, Qianjin Feng*. DGMSNet: Spine Segmentation for MR Image by a Detection-Guided Mixed-supervised Segmentation Network [J]. Medical Image Analysis, 2022, 102261.