Evaluation of different convolutional neural network encoder-decoder architectures for breast mass segmentation

Antti Isosalo; Henrik Mustonen; Topi Turunen; Pieta S. Ipatti; Jarmo Reponen; Miika T. Nieminen; Satu I. Inkinen

doi:10.1117/12.2628190

4 April 2022 Evaluation of different convolutional neural network encoder-decoder architectures for breast mass segmentation

Antti Isosalo, Henrik Mustonen, Topi Turunen, Pieta S. Ipatti, Jarmo Reponen, Miika T. Nieminen, Satu I. Inkinen

Author Affiliations +

Proceedings Volume 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications; 120370W (2022) https://doi.org/10.1117/12.2628190
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

In this work, we study convolutional neural network encoder-decoder architectures with pre-trained encoder weights for breast mass segmentation from digital screening mammograms. To automatically detect breast cancer, one fundamental task to achieve is the segmentation of the potential abnormal regions. Our objective was to find out whether encoder weights trained for breast cancer evaluation in comparison to those learned from natural images can yield a better model initialization, and furthermore improved segmentation results. We applied transfer learning and initialized the encoder, namely ResNet34 and ResNet22, with ImageNet weights and weights learned from breast cancer classification, respectively. A large clinically-realistic Finnish mammography screening dataset was utilized in model training and evaluation. Furthermore, an independent Portuguese INbreast dataset was utilized for further evaluation of the models. 5-fold cross-validation was applied for training. Soft Focal Tversky loss was used to calculate the model training time error. Dice score and Intersection over Union were used in quantifying the degree of similarity between the annotated and automatically produced segmentation masks. The best performing encoder-decoder with ResNet34 encoder tailed with U-Net decoder yielded Dice scores (mean±SD) of 0.7677±0.2134 for the Finnish dataset, and ResNet22 encoder tailed with U-Net decoder 0.8430±0.1091 for the INbreast dataset. No large differences in segmentation accuracy were found between the encoders initialized with weights pre-trained from breast cancer evaluation, and of those from natural image classification.

Citation Download Citation

Antti Isosalo, Henrik Mustonen, Topi Turunen, Pieta S. Ipatti, Jarmo Reponen, Miika T. Nieminen, and Satu I. Inkinen "Evaluation of different convolutional neural network encoder-decoder architectures for breast mass segmentation", Proc. SPIE 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications, 120370W (4 April 2022); https://doi.org/10.1117/12.2628190

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