Anatomical Plausibility in Deformable Image Registration Using Bayesian Optimization for Brain MRI Analysis

被引:0
作者
Castano-Aguirre, Mauricio [1 ]
Garcia, Hernan Felipe [2 ]
Cardenas-Pena, David [1 ]
Porras-Hurtado, Gloria Liliana [3 ]
Orozco-Gutierrez, Alvaro angel [1 ]
机构
[1] Technol Univ Pereira, Automat Res Grp, Pereira 660001, Colombia
[2] Univ Antioquia, SISTEM Res Grp, Medellin 050010, Colombia
[3] Salud Comfamiliar Caja Compensac Familiar Risarald, Pereira, Colombia
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 23期
关键词
Bayesian optimization; deformable registration; Gaussian processes; anatomical plausibility; LEARNING FRAMEWORK;
D O I
10.3390/app142310890
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Deformable image registration plays a crucial role in medical imaging by aligning anatomical structures across multiple datasets, which is essential for accurate diagnosis and treatment planning. However, existing deep learning-based deformable registration models often face challenges in ensuring anatomical plausibility, leading to unnatural deformations in critical brain structures. This paper proposes a novel framework that uses Bayesian optimization to address these challenges, focusing on registering 3D point clouds that represent brain structures. Our method uses probabilistic modeling to optimize non-rigid transformations, providing smooth and interpretable deformations that align with anatomical constraints. The proposed framework is validated using MRI data from patients diagnosed with hypoxic-ischemic encephalopathy (HIE) due to perinatal asphyxia. These datasets include brain scans taken at multiple time points, enabling the modeling of structural changes over time. By incorporating Bayesian optimization, we enhance the accuracy of the registration process while maintaining anatomical fidelity. Our results demonstrate that the approach provides interpretable, anatomically plausible deformations, outperforming conventional methods in terms of accuracy and reliability. This work offers an improved tool for brain MRI analysis, aiding healthcare professionals in better understanding disease progression and guiding therapeutic interventions.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] Robust Deformable Image Registration Using Cycle-Consistent Implicit Representations
    van Harten, Louis D.
    Stoker, Jaap
    Isgum, Ivana
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2024, 43 (02) : 784 - 793
  • [22] ADMIR-Affine and Deformable Medical Image Registration for Drug-Addicted Brain Images
    Tang, Kun
    Li, Zhi
    Tian, Lili
    Wang, Lihui
    Zhu, Yuemin
    [J]. IEEE ACCESS, 2020, 8 : 70960 - 70968
  • [23] Evaluation of image registration spatial accuracy using a Bayesian hierarchical model
    Liu, Suyu
    Yuan, Ying
    Castillo, Richard
    Guerrero, Thomas
    Johnson, Valen E.
    [J]. BIOMETRICS, 2014, 70 (02) : 366 - 377
  • [24] Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance
    Han, R.
    Jones, C. K.
    Lee, J.
    Wu, P.
    Vagdargi, P.
    Uneri, A.
    Helm, P. A.
    Luciano, M.
    Anderson, W. S.
    Siewerdsen, J. H.
    [J]. MEDICAL IMAGE ANALYSIS, 2022, 75
  • [25] A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences
    Alderliesten, Tanja
    Bosman, Peter A. N.
    Sonke, Jan-Jakob
    Bel, Arjan
    [J]. MEDICAL IMAGING 2014: IMAGE PROCESSING, 2014, 9034
  • [26] Image interpolation in 4D CT using a BSpline deformable registration model
    Schreibmann, E
    Chen, GTY
    Xing, L
    [J]. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2006, 64 (05): : 1537 - 1550
  • [27] Imposing implicit feasibility constraints on deformable image registration using a statistical generative model
    Sang, Yudi
    Xing, Xianglei
    Wu, Yingnian
    Ruan, Dan
    [J]. JOURNAL OF MEDICAL IMAGING, 2020, 7 (06)
  • [28] Automatic Document Image Binarization using Bayesian Optimization
    Vats, Ekta
    Hast, Anders
    Singh, Prashant
    [J]. PROCEEDINGS OF THE 4TH INTERNATIONAL WORKSHOP ON HISTORICAL DOCUMENT IMAGING AND PROCESSING, HIP 2017, 2017, : 89 - 94
  • [29] Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints
    Hu, Shunbo
    Zhang, Lintao
    Xu, Yan
    Shen, Dinggang
    [J]. MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2020, 2020, 12436 : 210 - 219
  • [30] 4D-CT deformable image registration using multiscale unsupervised deep learning
    Lei, Yang
    Fu, Yabo
    Wang, Tonghe
    Liu, Yingzi
    Patel, Pretesh
    Curran, Walter J.
    Liu, Tian
    Yang, Xiaofeng
    [J]. PHYSICS IN MEDICINE AND BIOLOGY, 2020, 65 (08)