CT-ORG, a new dataset for multiple organ segmentation in computed tomography.

Journal: Scientific data
Published Date: Nov 11, 2020

Abstract

Despite the relative ease of locating organs in the human body, automated organ segmentation has been hindered by the scarcity of labeled training data. Due to the tedium of labeling organ boundaries, most datasets are limited to either a small number of cases or a single organ. Furthermore, many are restricted to specific imaging conditions unrepresentative of clinical practice. To address this need, we developed a diverse dataset of 140 CT scans containing six organ classes: liver, lungs, bladder, kidney, bones and brain. For the lungs and bones, we expedited annotation using unsupervised morphological segmentation algorithms, which were accelerated by 3D Fourier transforms. Demonstrating the utility of the data, we trained a deep neural network which requires only 4.3 s to simultaneously segment all the organs in a case. We also show how to efficiently augment the data to improve model generalization, providing a GPU library for doing so. We hope this dataset and code, available through TCIA, will be useful for training and evaluating organ segmentation models.

Authors

  • Blaine Rister
    Stanford University, Department of Electrical Engineering, 1201 Welch Rd, Stanford, CA, 94305, USA. Electronic address: blaine@stanford.edu.
  • Darvin Yi
    Stanford University, Department of Radiology, Stanford, CA.
  • Kaushik Shivakumar
    Department of Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, CA, 94305, USA.
  • Tomomi Nobashi
    Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.

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