MISS-tool: medical image segmentation synthesis tool to emulate segmentation errors

Shuyue Guan; Ravi K. Samala; Arian Arab; Weijie Chen

doi:10.1117/12.2653650

7 April 2023 MISS-tool: medical image segmentation synthesis tool to emulate segmentation errors

Shuyue Guan, Ravi K. Samala, Arian Arab, Weijie Chen

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246518 (2023) https://doi.org/10.1117/12.2653650
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Segmentation of medical images with known ground truth is useful for investigating properties of performance metrics and comparing different approaches of combining multiple manual segmentations to establish a reference standard, thereby informing selection of performance metrics and truthing methods. For medical images, however, segmentation ground truth is typically not available. One way of synthesizing segmentation errors is to use regular geometric objects as ground truth, but they lack the complexity and variability of real anatomical objects. To address this problem, we developed a medical image segmentation synthesis (MISS)-tool. The MISS-tool emulates segmentations by adjusting truth masks of anatomical objects extracted from real medical images. We categorized six types of segmentation errors and developed contour transformation tools with a set of user-adjustable parameters to modify the defined truth contours to emulate different types of segmentation errors, thereby generating synthetic segmentations. In a simulation study, we synthesized multiple segmentations to emulate algorithms or observers with pre-defined sets of segmentation errors (e.g., under/over-segmentation) using 220 lung nodule cases from the LIDC lung computed tomography dataset. We verified that the synthetic segmentation results manifest the type of errors that are consistent with our pre-configured setting. Our tool is useful for synthesizing a range of segmentation errors within a clinical segmentation task.

Conference Presentation

Citation Download Citation

Shuyue Guan, Ravi K. Samala, Arian Arab, and Weijie Chen "MISS-tool: medical image segmentation synthesis tool to emulate segmentation errors", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246518 (7 April 2023); https://doi.org/10.1117/12.2653650

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available