Dataset for gastrointestinal tract segmentation on serial MRIs for abdominal tumor radiotherapyKaggle
Sangjune L. Lee,
Poonam Yadav,
Yin Li,
Jason J. Meudt,
Jessica Strang,
Dustin Hebel,
Alyx Alfson,
Stephanie J. Olson,
Tera R. Kruser,
Jennifer B. Smilowitz,
Kailee Borchert,
Brianne Loritz,
Laila Gharzai,
Shervin Karimpour,
John Bayouth,
Michael F. Bassetti
Affiliations
Sangjune L. Lee
Division of Radiation Oncology, Arthur Child Comprehensive Cancer Centre3395 Hospital Drive NW, Calgary, Alberta, T2N 5G2, Canada; Corresponding author.
Poonam Yadav
Department of Radiation Oncology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 675 North Saint Clair Street 21st Floor, Chicago, IL 60611, USA
Yin Li
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street Madison, WI 53706, USA
Jason J. Meudt
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Jessica Strang
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Dustin Hebel
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Alyx Alfson
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Stephanie J. Olson
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Tera R. Kruser
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Jennifer B. Smilowitz
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Kailee Borchert
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Brianne Loritz
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Laila Gharzai
Department of Radiation Oncology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 675 North Saint Clair Street 21st Floor, Chicago, IL 60611, USA
Shervin Karimpour
Department of Radiation Oncology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 675 North Saint Clair Street 21st Floor, Chicago, IL 60611, USA
John Bayouth
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Michael F. Bassetti
Department of Human Oncology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, USA
Purpose: Integrated MRI and linear accelerator systems (MR-Linacs) provide superior soft tissue contrast, and the capability of adapting radiotherapy plans to changes in daily anatomy. In this dataset, serial MRIs of the abdomen of patients undergoing radiotherapy were collected and the luminal gastro-intestinal tract was segmented to support an online segmentation algorithm competition. This dataset may be further utilized by radiation oncologists, medical physicists, and data scientists to further improve auto segmentation algorithms. Acquisition and validation of methods: Serial 0.35T MRIs from patients who were treated on an MR-Linac for tumors located in the abdomen were collected. The stomach, small intestine and large intestine were manually segmented on all MRIs by a team of annotators under the supervision of a board-certified radiation oncologist. Annotator segmentations were validated on 4 representative abdominal MRIs by comparing to the radiation oncologist's contours using 3D Hausdorff distance and 3D Dice coefficient metrics. Data format and usage notes: The dataset includes 467 de-identified scans and their contours from 107 patients. Each patient underwent 1–5 MRI scans of the abdomen. Most of the scans consisted of 144 axial slices with a pixel resolution of 1.5 × 1.5 × 3 mm, leading to 67,248 total slices in the dataset. Images in DICOM format were converted into Portable Graphics Format (PNG) files. Each Portable Graphics Format (PNG) image file stored a slice of the scan, with pixels recorded in 16 bits to cover the full range of intensity values. DICOM-RT segmentations were converted into Comma-Separated Values (CSV) format. Data including images and the annotations is publicly available at https://www.kaggle.com/ds/3577354. Potential applications: While manual segmentations are subject to bias and inter-observer variability, the dataset has been used for the UW-Madison GI Tract Image Segmentation Challenge hosted by Kaggle and may be used for ongoing segmentation algorithm development and potentially for dose accumulation algorithms.
