Predrag Bakic
Associate Professor
Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
Author
Editor
- Rebecca Fahrig
- John M. Sabol
- Ke Li
Summary, in English
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.
Department/s
- Radiology Diagnostics, Malmö
- LUCC: Lund University Cancer Centre
- Medical Radiation Physics, Malmö
- LU Profile Area: Light and Materials
- LTH Profile Area: Photon Science and Technology
- EpiHealth: Epidemiology for Health
Publishing year
2024
Language
English
Publication/Series
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume
12925
Document type
Conference paper
Publisher
SPIE
Topic
- Medical Image Processing
Keywords
- AI
- Breast phantom
- computer simulations and VCT
- segmentation
Conference name
Medical Imaging 2024: Physics of Medical Imaging
Conference date
2024-02-19 - 2024-02-22
Conference place
San Diego, United States
Status
Published
Research group
- Radiology Diagnostics, Malmö
- Medical Radiation Physics, Malmö
ISBN/ISSN/Other
- ISSN: 1605-7422
- ISBN: 9781510671546