TY - GEN
T1 - Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI
AU - Specktor-Fadida, Bella
AU - Yehuda, Bossmat
AU - Link-Sourani, Daphna
AU - Ben-Sira, Liat
AU - Ben-Bashat, Dafna
AU - Joskowicz, Leo
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain segmentation for gyrification assessment are particularly challenging because of the placenta fuzzy boundaries and the fetal brain cortex complex foldings. In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss. The loss is computed efficiently for each slice via erosion, dilation and XOR operators. We describe a new formulation of the loss akin to the Contour Dice metric. The combination of the Dice loss and the Contour Dice yielded the best performance for placenta segmentation. For fetal brain segmentation, the best performing loss was the combined Dice with Cross-Entropy loss followed by the Dice with Contour Dice loss, which performed better than other boundary losses.
AB - Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain segmentation for gyrification assessment are particularly challenging because of the placenta fuzzy boundaries and the fetal brain cortex complex foldings. In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss. The loss is computed efficiently for each slice via erosion, dilation and XOR operators. We describe a new formulation of the loss akin to the Contour Dice metric. The combination of the Dice loss and the Contour Dice yielded the best performance for placenta segmentation. For fetal brain segmentation, the best performing loss was the combined Dice with Cross-Entropy loss followed by the Dice with Contour Dice loss, which performed better than other boundary losses.
KW - Deep learning segmentation
KW - Fetal MRI
KW - Segmentation contour
UR - http://www.scopus.com/inward/record.url?scp=85151131787&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-25066-8_19
DO - https://doi.org/10.1007/978-3-031-25066-8_19
M3 - Conference contribution
SN - 9783031250651
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 355
EP - 368
BT - Computer Vision – ECCV 2022 Workshops, Proceedings
A2 - Karlinsky, Leonid
A2 - Michaeli, Tomer
A2 - Nishino, Ko
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th European Conference on Computer Vision, ECCV 2022
Y2 - 23 October 2022 through 27 October 2022
ER -