SegmentationReview: A Slicer3D extension for fast review of AI-generated segmentations

Anna Zapaishchykova; Divyanshu Tak; Aidan Boyd; Zezhong Ye; Hugo J.W.L. Aerts; Benjamin H. Kann

doi:10.1016/j.simpa.2023.100536

SegmentationReview: A Slicer3D extension for fast review of AI-generated segmentations

Anna Zapaishchykova, Divyanshu Tak, Aidan Boyd, Zezhong Ye, Hugo J.W.L. Aerts, Benjamin H. Kann^*

^*Corresponding author for this work

Research output: Contribution to journal › (Systematic) Review article › peer-review

Abstract

SegmentationReview is a package developed in Python for fast review and editing of biomedical image segmentations. Biomedical imaging segmentation quality assessment is a crucial part of the development medical artificial intelligence (AI) algorithms but is time-consuming and labor-intensive. SegmentationReview has several components that facilitate efficient segmentation review, including automated importing of lists of images and segmentations into Slicer3D, a user-friendly graphical user interface for reviewing and assessing the quality of the segmentation, and automated tabular data-saving. The package has been tested and released as an open-source extension for Slicer3D. It enables fast, user-friendly review and editing for biomedical image segmentations.

Original language	English
Article number	100536
Number of pages	3
Journal	Software Impacts
Volume	17
Issue number	1
DOIs	https://doi.org/10.1016/j.simpa.2023.100536
Publication status	Published - 1 Sept 2023

Keywords

Artificial intelligence
Likert Score
Medical imaging
Segmentation
Slicer3D

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title = "SegmentationReview: A Slicer3D extension for fast review of AI-generated segmentations",

abstract = "SegmentationReview is a package developed in Python for fast review and editing of biomedical image segmentations. Biomedical imaging segmentation quality assessment is a crucial part of the development medical artificial intelligence (AI) algorithms but is time-consuming and labor-intensive. SegmentationReview has several components that facilitate efficient segmentation review, including automated importing of lists of images and segmentations into Slicer3D, a user-friendly graphical user interface for reviewing and assessing the quality of the segmentation, and automated tabular data-saving. The package has been tested and released as an open-source extension for Slicer3D. It enables fast, user-friendly review and editing for biomedical image segmentations.",

keywords = "Artificial intelligence, Likert Score, Medical imaging, Segmentation, Slicer3D",

author = "Anna Zapaishchykova and Divyanshu Tak and Aidan Boyd and Zezhong Ye and Aerts, {Hugo J.W.L.} and Kann, {Benjamin H.}",

note = "Funding Information: This work was supported by NIH/NIDCRK08DE030216. Funding Information: This work was supported by NIH/NIDCR K08DE030216 . Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = sep,

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doi = "10.1016/j.simpa.2023.100536",

language = "English",

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AU - Zapaishchykova, Anna

AU - Tak, Divyanshu

AU - Boyd, Aidan

AU - Ye, Zezhong

AU - Aerts, Hugo J.W.L.

AU - Kann, Benjamin H.

PY - 2023/9/1

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KW - Artificial intelligence

KW - Likert Score

KW - Medical imaging

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DO - 10.1016/j.simpa.2023.100536

M3 - (Systematic) Review article

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JO - Software Impacts

JF - Software Impacts

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SegmentationReview: A Slicer3D extension for fast review of AI-generated segmentations

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Keywords

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