Computer Number: 33

S. Mendoza, Z. Yang, J. Lamas, K. Jann, M. Harrington, J. Ringman, Y. Shi, D. Wang
USC, Los Angeles, United States

Impact: A robust cerebral small vessel density estimation method can help for large-scale analysis of black blood MRI data to look for possible biomarkers of early cognitive decline. Our pipeline will be shared with the community.

Computer Number: 34

O. Pulvéric, F. Ouadahi, T. Boutelier
Olea Medical, La Ciotat, France

Impact: By identifying a critical 3 mm threshold for registration errors, we established quantitative guidelines for acceptable registration accuracy in clinical workflows. This threshold helps define minimal performance requirements for registration algorithms when preprocessing images for segmentation tasks.

Computer Number: 35

X. Cao, Z. Zheng, Q. Yang
Academy for Engineering and Technology, Fudan University, Shanghai, China

Impact: This study significantly enhances the diagnostics of carotid atherosclerosis by leveraging high-resolution MRI and deep learning to accurately identify high-risk plaques, improving early intervention and potentially transforming outcomes in cardiovascular patient care.

Computer Number: 36

A. Cao, Z. Li, J. Jomsky, A. Laine, J. Guo
University of California, Santa Barbara, San Jose, United States

Impact: Our proposed novel deep learning model, MedSegMamba, reliably demonstrated state-of-the-art segmentation performance and utility across numerous datasets. It outperformed other well-established deep learning tools on the difficult tasks of subcortical and hippocampal subfield segmentation. Code is available here: https://github.com/aaroncao06/MedSegMamba.

Computer Number: 37

C. Ryu, S. Jung, Y. Choi, D-H Kim
Yonsei University, Seoul, Korea, Republic of

Impact: This approach improves MRI segmentation of motion-corrupted data, supporting reliable subcortical analysis without complex preprocessing. It provides a method for cleaner, artifact-resistant segmentation, presenting significant applications both in neurodevelopmental and neurodegenerative disease research.

Computer Number: 38

J. He, D. Li, B. Fu, L. Nie, R. Wang
Guizhou Provincial People’s Hospital, Guiyang, China

Impact: There is currently a lack of research on quantitative iron analysis in fine-grained thalamic subregions of AD patients. This study could provide new prognostic assessments and therapeutic target references for AD research.

Computer Number: 39

J. Bisbal, S. Jofré, P. Winter, A. Ponce, M. Aristova, J. Moore, O. Welin Odeback, S. Ansari, C. Tejos, S. Uribe, M. Markl, J. Sotelo, S. Schnell, D. Marlevi
Pontificia Universidad Catolica de Chile, Santiago, Chile

Impact: This study identifies Residual UNet as an effective tool for automated intracranial artery labeling, enabling fully automatic processing of intracranial 4D Flow MRI data.

Computer Number: 40

A. Kandpal, P. Prajapati, S. Maurya, R. Gupta, A. Singh
Indian Institute of Technology Delhi, New Delhi, India

Impact: The proposed deep-learning framework combines DWI images and ADC maps to enable automatic, rapid ischemic core segmentation with high accuracy, supporting radiologists in the objective evaluation and planning of stroke treatment.

Computer Number: 41

J. H. Legarreta, Z. Lan, Y. Chen, F. Zhang, E. Yeterian, N. Makris, J. Rushmore, Y. Rathi, L. O’Donnell
Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, Boston, United States

Impact: Diffusion MRI spherical mean should be considered as a relevant image contrast for cerebellar structure segmentation using deep neural networks towards an increasingly accurate connectivity analysis.

Computer Number: 42

A. Barat, R. Ramesh, A. Cacciola, A. Banerjee, M. Saranathan
University of Massachusetts Amherst, Amherst, United States

Impact: This robust thalamic nuclei segmentation tool can be integral for clinical neuroimaging, offering fast, reliable segmentation, opening the door for analysis of large public databases to study the role of thalamic nuclei in a variety of neurodegenerative and neuropsychiatric conditions.

Computer Number: 43

S. Misra, S. Bera, S. Basak, S. Sarkar, A. Rajan, S. Mohan, H. Poptani, S. Chawla, S. Bhaduri
TCG Centres for Research & Education in Science & Technology, Kolkata, India

Impact: It highlights potential of deep-learning models, particularly nnU-Net to improve accuracy and efficiency in brain-tumor segmentation, reducing reliance on labor-intensive methods like RANO and iRANO. Addressing dataset-specific limitations through transfer-learning, the findings aids in consistent tumor-volume assessment, enhancing treatment monitoring.

Computer Number: 44

S. Li, L. Jia, W. Cai, C. Wang, H. Wang, H. Li
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, Shanghai, China

Impact: This study implemented a semi-supervised learning approach to address the challenges of limited labeled data and high annotation costs in 3D fetal brain segmentation. The performance demonstrates the proposed algorithm's robust potential.

Computer Number: 45

A. Rezk, A. Al-Fakih, A. Shazly, K. Ryu, M. A. Al-masni
Sejong University, Seoul, Korea, Republic of

Impact: Our clinically guided, multi-decoder U-Net demonstrates improved segmentation accuracy, particularly in diverse and non-standard datasets. This innovation paves the way for more reliable, adaptable, and interpretable brain tumor imaging, enhancing diagnostic confidence and treatment planning.

Computer Number: 46

J. Barranco, A. Luyken, P. Stachs, O. Esteban, Y. Aleman, S. Langner, O. Stachs, B. Franceschiello, M. Bach Cuadra
CIBM Center for Biomedical Imaging, Lausanne, Switzerland

Impact: The publicly provided large-scale unbiased T1w MR-Eye atlases will facilitate spatial normalization and quantitative analysis in the field of ophthalmic imaging, helping clinicians in the diagnosis of multiple ocular diseases, and enhancing our understanding in sex-specific eye anatomy and physiology.

Computer Number: 47

E. Lotan, A. Saulnier, J. Nguyen, E. Hammon, A. Davis, M. Lee
NYU Grossman School of Medicine, New York, United States

Impact: This automated pipeline can enhance clinical decision-making and personalized treatment for glioblastoma patients. Its development will facilitate new research on imaging biomarkers, ultimately improving patient outcomes and advancing neuroimaging practices.

Computer Number: 48

S. Li, L. Guo, Y. Tian, G. Ju, S. Zhang, Y. Jin, X. Su, S. Tang, A. Zeng, Y. Luo, X. Yang, L. Wang, L. Wang, H. Zhang, W. Yang, X. Liang, Q. Yue
West China Hospital of Sichuan University, Chengdu, China

Impact: This work advances automated, multi-tumor segmentation tools, enhancing clinical workflows and supporting consistent, efficient CNS tumor analysis.