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Adjusting U-Net for the aortic abdominal aneurysm CT segmentation case
R.U. Epifanov 1, N.A. Nikitin 2, A.A. Rabtsun 3, L.N. Kurdyukov 3, A.A. Karpenko 2, R.I. Mullyadzhanov 1,4

1 Novosibirsk State University, 63090, Russia, Novosibirsk, Pirogov str., 2;
2 Meshalkin National Medical Research Center, 630055, Russia, Novosibirsk, Rechkunovskaya str., 15;
3 Novosibirsk State Medical University, 630091, Russia, Novosibirsk, Krasny ave., 52;
4 Institute of Thermophysics SB RAS, 630090, Russia, Novosibirsk, Lavrentyev ave., 1

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DOI: 10.18287/2412-6179-CO-1338

Страницы: 418-424.

Язык статьи: English.

Аннотация:
In this paper, we address the issue of developing of a convolutional neural network for the problem of aneurysm segmentation into three classes and of exploring ways for improving the quality of final segmentation masks. As a result of our study, macro dice score for classes of interest reaches 83.12% ± 4.27%. We explored different augmentation styles and showed the importance of applying intensity augmentation style to improve segmentation algorithm robustness in conditions of clinical data diversity. Augmentation with spatial and insensitive styles increase macro dice score up to 3%. The comparison of various inference mode indicate that combination of overlapping inference and segmentation window enlargement ameliorate macro dice up to 1.4%. Overall improvement of the quality of segmentation masks by macro dice score amounted up to 6% using combination of data-based augmentation style and advanced inference technique.

Ключевые слова:
abdominal aortic aneurysm, neural network, semantic segmentation, calcifications.

Благодарности
The work is supported by the Russian Science Foundation grant No. 21-15-00091.

Citation:
Epifanov RU, Nikitin NA, Rabtsun AA, Kurdyukov LN, Karpenko AA, Mullyadzhanov RI. Adjusting U-Net for the aortic abdominal aneurysm CT segmentation case. Computer Optics 2024; 48(3): 418-424. DOI: 10.18287/2412-6179-CO-1338.

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