5 bone segmentation masks and 15 annotations of anatomical landmarks for pelvis bones in each of 90 Computed Tomography (CT) cases extracted from the CT Lymph nodes and CT Colonography collections from the The Cancer Imaging Archive (TCIA).

Keywords: Radiology, Annotated, Pelvis, CT, Computed tomography, Anatomical landmarks, Bone segmentation.

Sample images

Sample images with reduced image quality. Please click to preview.

Dataset information

Short name CTPEL
Origin Clinical
Cite as Bryan Connolly and Chunliang Wang (2019) Segmented CT pelvis scans with annotated anatomical landmarks doi:10.23698/aida/ctpel
[BibTeX format]
Field Radiology
Organ Pelvis
Age span
Title Segmented CT pelvis scans with annotated anatomical landmarks
Author Bryan Connolly
Chunliang Wang
Year 2019
DOI doi:10.23698/aida/ctpel
Status Ongoing
Version 1.1
Scans 90
Annotations 1800
Size 28.0GB
Resolution
Modality CT
Scanner
Stain
Phase
References
  1. Wang C., Connolly B., de Oliveira Lopes P.F., Frangi A.F., Smedby Ö. (2019) Pelvis Segmentation Using Multi-pass U-Net and Iterative Shape Estimation. In: Vrtovec T., Yao J., Zheng G., Pozo J. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2018. Lecture Notes in Computer Science, vol 11404. Springer, Cham
Copyright Copyright 2019 KTH, Chunliang Wang
Access

Available under the following licenses, described in the License section below.

Controlled access
Free for use in research or education.

AIDA license
Free for use within AIDA.

Annotation

Segmentation was done first with an interactive software (Mialab), followed by manual inspection and correction using ITKSNAP. The interactive method is based on fuzzy connectedness followed by level set method. Both the segmentation mask and annotated anatomical landmarks were created by a trained radiologist.

File formats

DICOM. Please see our help pages for some examples of working with DICOM.

Importing binary masks to numpy

The dataset contains a DICOM SEG file, which can be converted to an ITK image file (mha, nifti, etc) by using dcmqi and the command line tool segimage2itkimage, which is straightforward to read into a numpy array.

Landmarks

Landmarks are available in the sr.dcm file and saved using the TID 1500 template.

License

Controlled access

Free for use in research or education.

Apply for access

Help with applying for controlled access.

AIDA BY license

Segmentation masks and anatomical landmark annotations

Copyright 2019 KTH, Chunliang Wang

Permission to use, copy, modify, and/or distribute the data within AIDA (Analytic Imaging Diagnostics Arena https://medtech4health.se/aida) for the purpose of research or education with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.

THE DATA IS PROVIDED “AS IS” AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS DATA INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR CHARACTERISTICS OF THIS DATA.

Attribution

In addition to the TCIA rules about using the image data, we would really appreciate if you include the following references in publications that make use of the provided segmentation masks or anatomical landmarks:

[1] Bryan Connolly and Chunliang Wang (2019) Segmented CT pelvis scans with annotated anatomical landmarks doi:10.23698/aida/ctpel.

[2] Wang C., Connolly B., de Oliveira Lopes P.F., Frangi A.F., Smedby Ö. (2019) Pelvis Segmentation Using Multi-pass U-Net and Iterative Shape Estimation. In: Vrtovec T., Yao J., Zheng G., Pozo J. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2018. Lecture Notes in Computer Science, vol 11404. Springer, Cham