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.

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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
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	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. Please contact AIDA for terms of access.

You are invited to send an access request email from your institutional account.

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To: aida-data@nbis.se
cc: chunwan@kth.se
Subject: Requesting access to dataset doi:10.23698/aida/ctpel
Hi!

I work at INSTITUTION department of DEPARTMENT in COUNTRY, emailing from my institutional account.

I would like to request access to the dataset doi:10.23698/aida/ctpel, for use in research or education.

Our planned use of the data can be summarized as:

BRIEF_DESCRIPTION_OF_PLANNED_ACTIVITIES_HERE

Dataset: https://datahub.aida.scilifelab.se/10.23698/aida/ctpel

Also, I would like to ALTERNATIVELY_WOULD_NOT_LIKE_TO be included in the public record of data sharing facilitated by AIDA:

https://docs.google.com/spreadsheets/d/1fl2BwZJ4rivOKzOCy5pAnxU8N1CyoF86BTCnH-rBV04

which is used to facilitate scientific discussion, and to show what good AIDA has been to the global research community.

(If "not", then only the institution/department information will be included. This choice of personal participation is not weighed into the access request eligibility evaluation.)

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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