This dataset contributes DICOM-converted annotations to the publicly available National Cancer Institute Imaging Data Commons [1] Prostate-MRI-US-Biopsy collection (https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy). Prostate-MRI-US-Biopsy collection was initially released by The Cancer Imaging Archive (TCIA) [2,3,4]. While the images in this collection are stored in the standard DICOM format, the collection is also accompanied by 1017 semi-automatic segmentations of the prostate and 1317 manual segmentations of target lesions in the STL format. Although STL is a common and practical format for 3D printing, it is not interoperable with many visualization and analysis tools commonly used in medical imaging research and does not provide any standard means to communicate metadata, among other limitations. This dataset contains segmentations of the prostate and target lesions harmonized into DICOM representation. Specifically, we created DICOM Encapsulated 3D Manufacturing Model objects (M3D modality) that includes the original STL content enriched with the DICOM metadata. Furthermore, we created an alternative encoding of the surface segmentations by rasterizing them and saving the result as a DICOM Segmentation object (SEG modality). As a result, the contributed DICOM objects can be stored in any DICOM server that supports those objects (including Google Healthcare DICOM stores), and the DICOM Segmentations can be visualized using off-the-shelf tools, such as OHIF Viewer. Conversion from STL to DICOM M3D modality was performed using PixelMed toolkit (https://www.pixelmed.com/dicomtoolkit.html). Conversion from STL to DICOM SEG was done in 2 steps. We used Slicer (https://www.slicer.org/) to rasterize the surface segmentation to the matrix of the segmented image, which were next converted to DICOM SEGs using dcmqi (https://github.com/QIICR/dcmqi) [5]. Resulting objects were validated using dicom3tools dciodvfy (https://www.dclunie.com/dicom3tools.html). Details describing the conversion process as well as the details on how to access the encapsulated STL content from the DICOM m3D files are provided in this GitHub repository: https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion. Specific files included in the record are: Prostate-MRI-US-Biopsy-DICOM-Annotations.zip: DICOM M3D and SEG files, organized into the folder hierarchy following this pattern: Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/%SeriesNumber-%Modality-%SeriesDescription.dcm referenced_images_sorted-idc_file_manifest.s5cmd: IDC manifest for downloading the T2W MRI images corresponding to the annotations. To download the files in this manifest, first install s5cmd (https://github.com/peak/s5cmd), and run the following command: s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run referenced_images_sorted-idc_file_manifest.s5cmd. Files will be organized in the Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/ folder hierarchy upon download. References [1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D. P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K. & Kikinis, R. NCI Imaging Data Commons. Cancer Res. 81, 4188–4193 (2021). doi: 10.1158/0008-5472.CAN-21-0950. [2] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A [3] Sonn GA, Natarajan S, Margolis DJ, MacAiran M, Lieu P, Huang J, Dorey FJ, Marks LS. Targeted biopsy in the detection of prostate cancer using an office based magnetic resonance ultrasound fusion device. Journal of Urology 189, no. 1 (2013): 86-91. DOI: 10.1016/j.juro.2012.08.095 [4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 [5] Herz, C., Fillion-Robin, J.-C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., Fichtinger, G., Pieper, S., Clunie, D., Kikinis, R. & Fedorov, A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Res. 77, e87–e90 (2017). DOI: 10.1158/0008-5472.CAN-17-0336.