Wu Jiong, Yang Qi, Zhou Shuang
School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, 415000, Hunan, China.
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
Int J Comput Assist Radiol Surg. 2023 Apr;18(4):621-628. doi: 10.1007/s11548-022-02788-9. Epub 2022 Nov 8.
Cross-sequence magnetic resonance image (MRI) registration and segmentation are two essential steps in a variety of medical image analysis tasks. And have attracted considerable research interest. However, they remain challenging due to domain shifts between different sequences. This study is aiming at proposing a novel method via disentangled representations, latent shape image learning (LSIL), for cross-sequence image registration and segmentation.
Images from different sequences were firstly decomposed into a shared domain-invariant shape space and a domain-specific appearance space via an unsupervised image-to-image translation approach. A latent shape image learning model is then built on the disentangled shape representations to generate latent shape images. A series of experiments including cross-sequence image registration and segmentation were performed to qualitatively and quantitatively verify the validity of our method. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were adopted as our evaluation metrics.
The performance of our method was evaluated based on 2 datasets total of 50 MRIs. The experimental results showed the superiority of the proposed framework over the state-of-the-art cross-sequence registration and segmentation approaches. The proposed method shows the mean DSCs of 0.711 and 0.867, respectively, in cross-sequence registration and segmentation.
We proposed a novel method based on representation disentangling to solve the cross-sequence registration and segmentation problem. Experimental results prove the feasibility and generalization of the generated latent shape images. The proposed method demonstrates significant potential for use in clinical environments of missing sequences. The source code is available at https://github.com/wujiong-hub/LSIL .
跨序列磁共振成像（MRI）配准和分割是各种医学图像分析任务中的两个关键步骤，并且已经引起了相当多的研究兴趣。然而，由于不同序列之间的域偏移，它们仍然具有挑战性。本研究旨在通过解缠表示，即潜在形状图像学习（LSIL），提出一种用于跨序列图像配准和分割的新方法。
首先通过无监督图像到图像的翻译方法，将来自不同序列的图像分解为共享的域不变形状空间和特定于域的外观空间。然后基于解缠的形状表示构建潜在形状图像学习模型，以生成潜在形状图像。进行了一系列实验，包括跨序列图像配准和分割，以定性和定量地验证我们方法的有效性。采用骰子相似系数（DSC）和第95百分位数豪斯多夫距离（HD95）作为我们的评估指标。
我们的方法基于总共50个MRI的2个数据集进行了性能评估。实验结果表明，所提出的框架优于现有的跨序列配准和分割方法。所提出的方法在跨序列配准和分割中分别显示出平均DSC为0.711和0.867。
我们提出了一种基于表示解缠的新方法来解决跨序列配准和分割问题。实验结果证明了所生成的潜在形状图像的可行性和通用性。所提出的方法在缺失序列的临床环境中显示出显著的应用潜力。源代码可在https://github.com/wujiong-hub/LSIL获取。
