TY - GEN
T1 - Two-Stage Deep Learning Segmentation for Tiny Brain Regions
AU - Ren, Yan
AU - Zheng, Xiawu
AU - Ji, Rongrong
AU - Chen, Jie
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
PY - 2024
Y1 - 2024
N2 - Accurate segmentation of brain regions has become increasingly important in the early diagnosis of brain diseases. Widely used methods for brain region segmentation usually rely on atlases and deformations, which require manual intervention and do not focus on tiny object segmentation. To address the challenge of tiny brain regions segmentation, we propose a two-stage segmentation network based on deep learning, using both 2D and 3D convolution. We first introduce the concept of the Small Object Distribution Map (SODM), allowing the model to perform coarse-to-fine segmentation for objects of different scales. Then, a contrastive loss function is implemented to automatically mine difficult negative samples, and two attention modules are added to assist in the accurate generation of the small object distribution map. Experimental results on a dataset of 120 brain MRI demonstrate that our method outperforms existing approaches in terms of objective evaluation metrics and subjective visual effects and shows promising potential for assisting in the diagnosis of brain diseases.
AB - Accurate segmentation of brain regions has become increasingly important in the early diagnosis of brain diseases. Widely used methods for brain region segmentation usually rely on atlases and deformations, which require manual intervention and do not focus on tiny object segmentation. To address the challenge of tiny brain regions segmentation, we propose a two-stage segmentation network based on deep learning, using both 2D and 3D convolution. We first introduce the concept of the Small Object Distribution Map (SODM), allowing the model to perform coarse-to-fine segmentation for objects of different scales. Then, a contrastive loss function is implemented to automatically mine difficult negative samples, and two attention modules are added to assist in the accurate generation of the small object distribution map. Experimental results on a dataset of 120 brain MRI demonstrate that our method outperforms existing approaches in terms of objective evaluation metrics and subjective visual effects and shows promising potential for assisting in the diagnosis of brain diseases.
KW - Brain Region Segmentation
KW - Small Object Distribution Map
KW - Two-stage Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85181768988&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8558-6_15
DO - 10.1007/978-981-99-8558-6_15
M3 - Conference contribution
AN - SCOPUS:85181768988
SN - 9789819985579
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 174
EP - 184
BT - Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings
A2 - Liu, Qingshan
A2 - Wang, Hanzi
A2 - Ji, Rongrong
A2 - Ma, Zhanyu
A2 - Zheng, Weishi
A2 - Zha, Hongbin
A2 - Chen, Xilin
A2 - Wang, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023
Y2 - 13 October 2023 through 15 October 2023
ER -