Dataset

This dataset was created by Soumik Rakshit

Contents

A platform for end-to-end development of machine learning solutions in biomedical imaging. Challenges in medical image analysis became popular after the organization of the Grand Challenges for Medical Image Analysis at the MICCAI conference in 2007. Hosting challenge events quickly became commonplace for conferences: MICCAI, ISBI, and SPIE Medical Imaging, amongst others, have hosted challenge events. Leading journals such as IEEE Transactions on Medical Imaging and Medical Image Analysis have welcomed overview papers that described the results of individual challenges.

Maintaining a challenge, so that new submissions are quickly processed upon submission, is a lot of work. Typically, a junior researcher at some institution is responsible for maintaining a challenge website, but at some point the researcher moves on and the site is no longer kept up-to-date.

Grand Challenge was created in 2010 to make it easy for organizers of challenges to set up a website for a particular challenge. Its aim was to bring all information on challenges in the domain of biomedical image analysis available in a single place. In 2012 we switched to Django web framework, marking 2012 as our founding year.

This data set was created for use in the NCI-ISBI 2013 Challenge - Automated Segmentation of Prostate Structures. The challenge data set was divided into 3 parts including training, leaderboard and test data sets. This allowed participants to prepare their algorithms and test their results prior to submitting to a final test phase for selecting the winner. Image data were selected from PROSTATE-DIAGNOSIS and Prostate-3T collections on TCIA. Cases consist of axial scans with half obtained at 1.5 T (Philips Achieva) with an endo-rectal receiver coil (fromBostonMedicalCenter) and the other half at 3T (Siemens TIM) with a surface coil (from Radboud University Nijmegen Medical Centre [RUNMC],Netherlands). They were acquired as T2-weighted MR axial pulse sequences with either 4 mm thick slices at 3T or 3 mm thick at 1.5T. Each file contains nearly all DICOM acquisition parameters except tags that specifically identify Private Health Information. Each case has had central gland (CG) and peripheral zone (PZ) outlines marked by Drs. Nicolas Bloch (Boston University School of Medicine) and Mirabela Rusu (Case Western University) or Drs. Henkjan Huisman, Geert Litjens, or Jurgen Futterer at RUNMC Netherlands.

The Cell Tracking Challenge (CTC) was launched in 2012, with the aim of fostering the development of novel, robust cell segmentation and tracking algorithms, and helping the developers with the evaluation of their new algorithmic developments. Over its more than a decade long existence, six fixed-deadline ISBI challenge editions have been organized, and since February 2017, the challenge is open for online submissions that are monthly evaluated, ranked, and posted on the challenge website. So far, two benchmarks have been offered, namely segmentation-and-tracking benchmark (evaluating segmentation and tracking performance) and segmentation-only benchmark (evaluating purely segmentation performance, no tracking part is required). A detailed description of the focus and history of the CTC can be found at http://celltrackingchallenge.net/ and in the new open-access Nature Methods summary of the 10 years of its existence. The CTC is in constant evolution, and - as we did in the previous six editions attached to ISBI 2013-2015 and ISBI 2019-2021 - we plan to introduce some novelties in this new ISBI-sponsored challenge event. Specifically, in this new 7th edition, the participants will be encouraged to submit further solutions to the recently opened generalizability tasks - either in the frame of thesegmentation-and-tracking benchmark (Task 1) or the segmentation-only benchmark (Task 2). The generalizability tasks focus on the development of methods that exhibit better generalizability and work across most, if not all, of the existing datasets, instead of being optimized for one or a few datasets only. These tasks were established for the ISBI 2021 edition, and their first results were reported in the abovementioned paper, but no further results have been received since then. Furthermore, a new tracking-only - more precisely linking-only benchmark (Cell Linking Benchmark) will be introduced to complement the segmentation-only benchmark for those who want to evaluate purely the object linking methods without having to supply segmentation results. Such a benchmark has been missing in the CTC portfolio and it is demanded by the CTC participants and the scientific community at large. Participants will be encouraged to supply ideally generalizable solutions (Task 3) working across 13 preselected datasets but will also be able to submit dataset-specific solutions (Task 4) for datasets of their choice.

Summary

Prostate transversal T2-weighted magnetic resonance images (MRIs) acquired on a 3.0T Siemens TrioTim using only a pelvic phased-array coil were acquired for prostate cancer detection. The data was provided to TCIA as part of an ISBI challenge competition in 2013.

• Data Access
• Detailed Description
• Citations & Data Usage Policy
• Versions

Citations & Data Usage Policy

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations:

Data Citation

Litjens, Geert, Futterer, Jurgen, & Huisman, Henkjan. (2015). Data From Prostate-3T. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2015.QJTV5IL5

TCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. (2013) The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6 pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Other Publications Using This Data

• Bloch N, Rusu M, et al. (2015) NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures. TCIA. St. Louis, MO

TCIA maintains a list of publications that leverage our data. If you have a publication you'd like to add, please contact the TCIA Helpdesk.

Prostate cancer T1- and T2-weighted magnetic resonance images (MRIs) were acquired on a 1.5 T Philips Achieva by combined surface and endorectal coil, including dynamic contrast-enhanced images obtained prior to, during and after I.V. administration of 0.1 mmol/kg body weight of Gadolinium-DTPA (pentetic acid). Corresponding clinical metadata (XLS format) and 3D segmentation files (NRRD format) are offered as a supplement to this image collection. The XLS file contains pathology biopsy and excised gland tissue reports and the MRI radiology report for most subjects.

The Multi-component NRRD Segmentations allow visualization and downstream analysis in 3D Slicer of the following prostate components: prostate gland boundary; internal capsule; central gland, peripheral zone; seminal vesicles; urethra; cancer – dominant nodule; neurovascular bundle; penile bulb; ejaculatory duct; veru-montanum; and rectum. See our tutorial on Using 3D Slicer with the Prostate-Diagnosis data if you are not familiar with using this kind of data.

The Seminal vesicles (SV) and neurovascular bundle (NVB) Segmentations delineate the neurovascular bundle and seminal vessicles as MHA files. These were provided as part of a planned challenge competition that did not materialize.

The Third Party Analysis dataset mentioned beneath the Data Access table was added later as part of the NCI-ISBI 2013 Challenge - Automated Segmentation of Prostate Structures. It includes segmentations for 30 Prostate-Diagnosis subjects in NRRD format which mark the boundaries of the central gland and peripheral zone were also provided

Prostate transversal T2-weighted magnetic resonance images (MRIs) acquired on a 3.0T Siemens TrioTim using only a pelvic phased-array coil were acquired for prostate cancer detection. The data was provided to TCIA as part of an ISBI challenge competition in 2013.