Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation

Hanna Tomic; Zhikai Yang; Anders Tingberg; Sophia Zackrisson; Rodrigo Moreno; Örjan Smedby; Magnus Dustler; Predrag Bakic

doi:10.1117/12.3008840

1 April 2024 Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation

Hanna Tomic, Zhikai Yang, Anders Tingberg, Sophia Zackrisson, Rodrigo Moreno, Örjan Smedby, Magnus Dustler, Predrag Bakic

Author Affiliations +

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129251P (2024) https://doi.org/10.1117/12.3008840
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Segmentation of diagnostic radiography images using deep learning is progressively expanding, which sets demands on the accessibility, availability, and accuracy of the software tools used. This study aimed at evaluating the performance of a segmentation model for digital breast tomosynthesis (DBT), with the use of computer-simulated breast anatomy. We have simulated breast anatomy and soft tissue breast lesions, by utilizing a model approach based on the Perlin noise algorithm. The obtained breast phantoms were projected and reconstructed into DBT slices using a publicly available open-source reconstruction method. Each lesion was then segmented using two approaches: 1. the Segment Anything Model (SAM), a publicly available AI-based method for image segmentation and 2. manually by three human observers. The lesion area in each slice was compared to the ground truth area, derived from the binary mask of the lesion model. We found similar performance between SAM and manual segmentation. Both SAM and the observers performed comparably in the central slice (mean absolute relative error compared to the ground truth and standard deviation SAM: 4±3%, observers: 3±3%). Similarly, both SAM and the observers overestimated the lesion area in the peripheral reconstructed slices (mean absolute relative error and standard deviation SAM: 277±190%, observers: 295±182%). We showed that 3D voxel phantoms can be used for evaluating different segmentation methods. In preliminary comparison, tumor segmentation in simulated DBT images using SAM open-source method showed a similar performance as manual tumor segmentation.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hanna Tomic, Zhikai Yang, Anders Tingberg, Sophia Zackrisson, Rodrigo Moreno, Örjan Smedby, Magnus Dustler, and Predrag Bakic "Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129251P (1 April 2024); https://doi.org/10.1117/12.3008840

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