Brain midline segmentation method based on prior knowledge and path optimization_Journal of Biomedical Engineering

Authors：

GENG Shuai ¹ , LI Yonghui ¹ , AO Yu ¹ , SHI Weili ^1,3 , MIAO Yu ¹ , WANG Shuhan ⁴ ,  JIANG Zhengang ^1,2

1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, P. R. China;
2. Jilin Provincial Key Laboratory of Intelligent Computing in Medical Image, Changchun University of Science and Technology, Changchun 130022, P. R. China;
3. Jilin Province Cross-regional Cooperation Science and Technology Innovation Center of Intelligent Technology and Instrument for Precise Diagnosis and Treatment, Changchun University of Science and Technology, Changchun 130022, P. R. China;
4. Encephalopathy Department, Xiangshui County Hospital of Traditional Chinese Medicine, Yancheng, Jiangsu 224000, P. R. China;

Corresponding author：

JIANG Zhengang, Email: jiangzhengang@cust.edu.cn

Keywords：

Brain midline; Medical image segmentation; Prior knowledge; Synergistic fusion; Path searching

DOI：

10.7507/1001-5515.202412032

Video：

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To address the challenges faced by current brain midline segmentation techniques, such as insufficient accuracy and poor segmentation continuity, this paper proposes a deep learning network model based on a two-stage framework. On the first stage of the model, prior knowledge of the feature consistency of adjacent brain midline slices under normal and pathological conditions is utilized. Associated midline slices are selected through slice similarity analysis, and a novel feature weighting strategy is adopted to collaboratively fuse the overall change characteristics and spatial information of these associated slices, thereby enhancing the feature representation of the brain midline in the intracranial region. On the second stage, the optimal path search strategy for the brain midline is employed based on the network output probability map, which effectively addresses the problem of discontinuous midline segmentation. The method proposed in this paper achieved satisfactory results on the CQ500 dataset provided by the Center for Advanced Research in Imaging, Neurosciences and Genomics, New Delhi, India. The Dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), and normalized surface Dice (NSD) were 67.38 ± 10.49, 24.22 ± 24.84, 1.33 ± 1.83, and 0.82 ± 0.09, respectively. The experimental results demonstrate that the proposed method can fully utilize the prior knowledge of medical images to effectively achieve accurate segmentation of the brain midline, providing valuable assistance for subsequent identification of the brain midline by clinicians.

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1. Caceres J A, Goldstein J N. Intracranial hemorrhage. Emergency Medicine Clinics of North America, 2012, 30(3): 771-794.
2. Hemphill J C 3rd, Greenberg S M, Anderson C S, et al. Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 2015, 46(7): 2032-2060.
3. Vitt J R, Sun C H, Le Roux P D, et al. Minimally invasive surgery for intracerebral hemorrhage. Current Opinion in Critical Care, 2020, 26(2): 129-136.
4. Liao C C, Chiang I J, Xiao F, et al. Tracing the deformed midline on brain CT. Biomedical Engineering: Applications, Basis and Communications, 2006, 18(6): 305-311.
5. Liu R, Li S, Su B, et al. Automatic detection and quantification of brain midline shift using anatomical marker model. Computerized Medical Imaging and Graphics, 2014, 38(1): 1-14.
6. Chen W, Najarian K, Ward K. Actual midline estimation from brain CT scan using multiple regions shape matching//2010 20th International Conference on Pattern Recognition, Istanbul: IEEE, 2010: 2552-2555.
7. Qi X. Automated midline shift detection on brain CT images for computer-aided clinical decision support. Richmond: Virginia Commonwealth University, 2013.
8. Patel S. An overview and application of deep convolutional neural networks for medical image segmentation//2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore: IEEE, 2023: 722-728.
9. Takahashi S, Sakaguchi Y, Kouno N, et al. Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. Journal of Medical Systems, 2024, 48(1): 84.
10. Luo X , WU Y, Chen J. Research progress on deep learning methods for object detection and semantic segmentation in UAV aerial images. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 28822.
11. Jian M, Wu R, Fu L, et al. Dual-branch-UNnet: a dual-branch convolutional neural network for medical image segmentation. Comput Model Eng Sci, 2023, 137(1): 705-716.
12. Wu D, Li H, Chang J, et al. Automatic brain midline surface delineation on 3D CT images with intracranial hemorrhage. IEEE Transactions on Medical Imaging, 2022, 41(9): 2217-2227.
13. Wang S, Liang K, Pan C, et al. Segmentation-based method combined with dynamic programming for brain midline delineation//2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa: IEEE, 2020: 772-776.
14. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention, Munich: MICCAI, 2015: 234-241.
15. Isensee F, Jäger P F, Kohl S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods, 2021, 18(2): 203-211.
16. González C, Ranem A, Pinto Dos Santos D, et al. Lifelong nnU-Net: a framework for standardized medical continual learning. Scientific Reports, 2023, 13(1): 9381.
17. Somasundaram A, Wu M, Reik A, et al. Evaluating sex-specific differences in abdominal fat volume and proton density fat fraction at MRI using automated nnU-Net–based segmentation. Radiol Artif Intell, 2024, 6(4): e230471.
18. Dot G, Schouman T, Dubois G, et al. Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework. European Radiology, 2022, 32(6): 3639-3648.
19. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Medical Image Computing and Computer-Assisted Intervention, Athens: MICCAI, 2016: 424-432.
20. Chilamkurthy S, Ghosh R, Tanamala S, et al. Development and validation of deep learning algorithms for detection of critical findings in head CT scans. arXiv preprint, 2018, arXiv: 1803.05854.
21. Ke J, Lv Y, Ma F, et al. Deep learning-based approach for the automatic segmentation of adult and pediatric temporal bone computed tomography images. Quantitative Imaging in Medicine and Surgery, 2023, 13(3): 1577-1591.
22. Yang J, Wickramasinghe U, Ni B, et al. Implicitatlas: learning deformable shape templates in medical imaging//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans: IEEE, 2022: 15861-15871.
23. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-Net: learning where to look for the pancreas. arXiv preprint, 2018, arXiv: 1804.03999.

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