Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.02784 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912898248343552 |
|---|---|
| author | Zalevskyi, Vladyslav Sanchez, Thomas Kaandorp, Misha Roulet, Margaux Fajardo-Rojas, Diego Li, Liu Hutter, Jana Li, Hongwei Bran Barkovich, Matthew Ji, Hui Wilhelmi, Luca Dändliker, Aline Steger, Céline Koob, Mériam Gomez, Yvan Jakovčić, Anton Klaić, Melita Adžić, Ana Marković, Pavel Grabarić, Gracia Rados, Milan Verdera, Jordina Aviles Kasprian, Gregor Dovjak, Gregor Gaubert-Rachmühl, Raphael Aschwanden, Maurice Zeng, Qi Karimi, Davood Peruzzo, Denis Ciceri, Tommaso Longari, Giorgio Hamadache, Rachika E. Bouzid, Amina Lladó, Xavier Chiarella, Simone Martí-Juan, Gerard Ballester, Miguel Ángel González Castellaro, Marco Pinamonti, Marco Visani, Valentina Cremese, Robin Sam, Keïn Gaudfernau, Fleur Ahir, Param Parikh, Mehul Zenk, Maximilian Baumgartner, Michael Maier-Hein, Klaus Tianhong, Li Hong, Yang Longfei, Zhao Preloznik, Domen Špiclin, Žiga Choi, Jae Won Li, Muyang Fu, Jia Wang, Guotai Jiang, Jingwen Tong, Lyuyang Du, Bo Gondova, Andrea You, Sungmin Im, Kiho Qayyum, Abdul Mazher, Moona Niederer, Steven A Jakab, Andras Licandro, Roxane Payette, Kelly Cuadra, Meritxell Bach |
| author_facet | Zalevskyi, Vladyslav Sanchez, Thomas Kaandorp, Misha Roulet, Margaux Fajardo-Rojas, Diego Li, Liu Hutter, Jana Li, Hongwei Bran Barkovich, Matthew Ji, Hui Wilhelmi, Luca Dändliker, Aline Steger, Céline Koob, Mériam Gomez, Yvan Jakovčić, Anton Klaić, Melita Adžić, Ana Marković, Pavel Grabarić, Gracia Rados, Milan Verdera, Jordina Aviles Kasprian, Gregor Dovjak, Gregor Gaubert-Rachmühl, Raphael Aschwanden, Maurice Zeng, Qi Karimi, Davood Peruzzo, Denis Ciceri, Tommaso Longari, Giorgio Hamadache, Rachika E. Bouzid, Amina Lladó, Xavier Chiarella, Simone Martí-Juan, Gerard Ballester, Miguel Ángel González Castellaro, Marco Pinamonti, Marco Visani, Valentina Cremese, Robin Sam, Keïn Gaudfernau, Fleur Ahir, Param Parikh, Mehul Zenk, Maximilian Baumgartner, Michael Maier-Hein, Klaus Tianhong, Li Hong, Yang Longfei, Zhao Preloznik, Domen Špiclin, Žiga Choi, Jae Won Li, Muyang Fu, Jia Wang, Guotai Jiang, Jingwen Tong, Lyuyang Du, Bo Gondova, Andrea You, Sungmin Im, Kiho Qayyum, Abdul Mazher, Moona Niederer, Steven A Jakab, Andras Licandro, Roxane Payette, Kelly Cuadra, Meritxell Bach |
| contents | Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry prediction as a new task alongside tissue segmentation. For the first time, our diverse multi-centric test set included data from a new low-field (0.55T) MRI dataset. Evaluation metrics were also expanded to include the topology-specific Euler characteristic difference (ED). Sixteen teams submitted segmentation methods, most of which performed consistently across both high- and low-field scans. However, longitudinal trends indicate that segmentation accuracy may be reaching a plateau, with results now approaching inter-rater variability. The ED metric uncovered topological differences that were missed by conventional metrics, while the low-field dataset achieved the highest segmentation scores, highlighting the potential of affordable imaging systems when paired with high-quality reconstruction. Seven teams participated in the biometry task, but most methods failed to outperform a simple baseline that predicted measurements based solely on gestational age, underscoring the challenge of extracting reliable biometric estimates from image data alone. Domain shift analysis identified image quality as the most significant factor affecting model generalization, with super-resolution pipelines also playing a substantial role. Other factors, such as gestational age, pathology, and acquisition site, had smaller, though still measurable, effects. Overall, FeTA 2024 offers a comprehensive benchmark for multi-class segmentation and biometry estimation in fetal brain MRI, underscoring the need for data-centric approaches, improved topological evaluation, and greater dataset diversity to enable clinically robust and generalizable AI tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge Zalevskyi, Vladyslav Sanchez, Thomas Kaandorp, Misha Roulet, Margaux Fajardo-Rojas, Diego Li, Liu Hutter, Jana Li, Hongwei Bran Barkovich, Matthew Ji, Hui Wilhelmi, Luca Dändliker, Aline Steger, Céline Koob, Mériam Gomez, Yvan Jakovčić, Anton Klaić, Melita Adžić, Ana Marković, Pavel Grabarić, Gracia Rados, Milan Verdera, Jordina Aviles Kasprian, Gregor Dovjak, Gregor Gaubert-Rachmühl, Raphael Aschwanden, Maurice Zeng, Qi Karimi, Davood Peruzzo, Denis Ciceri, Tommaso Longari, Giorgio Hamadache, Rachika E. Bouzid, Amina Lladó, Xavier Chiarella, Simone Martí-Juan, Gerard Ballester, Miguel Ángel González Castellaro, Marco Pinamonti, Marco Visani, Valentina Cremese, Robin Sam, Keïn Gaudfernau, Fleur Ahir, Param Parikh, Mehul Zenk, Maximilian Baumgartner, Michael Maier-Hein, Klaus Tianhong, Li Hong, Yang Longfei, Zhao Preloznik, Domen Špiclin, Žiga Choi, Jae Won Li, Muyang Fu, Jia Wang, Guotai Jiang, Jingwen Tong, Lyuyang Du, Bo Gondova, Andrea You, Sungmin Im, Kiho Qayyum, Abdul Mazher, Moona Niederer, Steven A Jakab, Andras Licandro, Roxane Payette, Kelly Cuadra, Meritxell Bach Computer Vision and Pattern Recognition Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry prediction as a new task alongside tissue segmentation. For the first time, our diverse multi-centric test set included data from a new low-field (0.55T) MRI dataset. Evaluation metrics were also expanded to include the topology-specific Euler characteristic difference (ED). Sixteen teams submitted segmentation methods, most of which performed consistently across both high- and low-field scans. However, longitudinal trends indicate that segmentation accuracy may be reaching a plateau, with results now approaching inter-rater variability. The ED metric uncovered topological differences that were missed by conventional metrics, while the low-field dataset achieved the highest segmentation scores, highlighting the potential of affordable imaging systems when paired with high-quality reconstruction. Seven teams participated in the biometry task, but most methods failed to outperform a simple baseline that predicted measurements based solely on gestational age, underscoring the challenge of extracting reliable biometric estimates from image data alone. Domain shift analysis identified image quality as the most significant factor affecting model generalization, with super-resolution pipelines also playing a substantial role. Other factors, such as gestational age, pathology, and acquisition site, had smaller, though still measurable, effects. Overall, FeTA 2024 offers a comprehensive benchmark for multi-class segmentation and biometry estimation in fetal brain MRI, underscoring the need for data-centric approaches, improved topological evaluation, and greater dataset diversity to enable clinically robust and generalizable AI tools. |
| title | Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.02784 |