Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Dakahlia Governorate, Egypt.
Computer Engineering and Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Dakahlia Governorate, Egypt.
Med Biol Eng Comput. 2020 Sep;58(9):2161-2175. doi: 10.1007/s11517-020-02225-6. Epub 2020 Jul 17.
The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most frequent image modality used to evaluate such lesions. Because of the massive amount of data, manual segmentation cannot be achieved within a sensible time that restricts the usage of accurate quantitative measurement in clinical practice. Therefore, the need for effective automated segmentation techniques is critical. However, a large spatial variability between the structure of brain lesions makes it more challenging. Recently, convolutional neural network (CNN), in particular, the region-based CNN (R-CNN), have attained tremendous progress within the field of object recognition because of its ability to learn and represent features. CNN has proven a last-breaking performance in various fields, such as object recognition, and has also gained more attention in brain imaging, especially in tissue and brain segmentation. In this paper, an automated technique for MS lesion segmentation is proposed, which is built on a 3D patch-wise R-CNN. The proposed system includes two stages: first, segmenting MS lesions in T2-w and FLAIR sequences using R-CNN, then an adaptive neuro-fuzzy inference system (ANFIS) is applied to fuse the results of the two modalities. To evaluate the performance of the proposed method, the public MICCAI2008 MS challenge dataset is employed to segment MS lesions. The experimental results show competitive results of the proposed method compared with the state-of-the-art MS lesion segmentation methods with an average total score of 83.25 and an average sensitivity of 61.8% on the MICCAI2008 testing set. Graphical Abstract The proposed system overview. First, the input of two modalities FLAIR and T2 are pre-processed to remove the skull and correct the bias field. Then 3D patches for lesion and non-lesion tissues are extracted and fed to R-CNN. Each R-CNN produces a probability map of the segmentation result that provides to ANFIS to fuse the results and obtain the final MS lesion segmentation. The MS lesions are shown on a pre-processed FLAIR image.
病灶分割在多发性硬化症（MS）的诊断和监测中起着核心作用。磁共振成像（MRI）是最常用于评估此类病灶的影像模态。由于数据量巨大，手动分割无法在合理的时间内完成，这限制了准确的定量测量在临床实践中的应用。因此，需要有效的自动分割技术。然而，脑病变结构的大量空间变异性使得这一任务更加具有挑战性。最近，卷积神经网络（CNN），特别是基于区域的 CNN（R-CNN），在物体识别领域取得了巨大的进展，因为它具有学习和表示特征的能力。CNN 在物体识别等各个领域都取得了突破性的性能，在脑成像领域也越来越受到关注，特别是在组织和脑分割方面。本文提出了一种基于 3D 补丁式 R-CNN 的 MS 病灶自动分割技术。该系统包括两个阶段：首先，使用 R-CNN 对 T2-w 和 FLAIR 序列中的 MS 病灶进行分割，然后应用自适应神经模糊推理系统（ANFIS）融合两种模态的结果。为了评估所提出方法的性能，使用公共 MICCAI2008 MS 挑战赛数据集来分割 MS 病灶。实验结果表明，与最先进的 MS 病灶分割方法相比，该方法具有竞争力，在 MICCAI2008 测试集上的平均总分为 83.25，平均灵敏度为 61.8%。
图 1 所提出系统的概述。首先，对 FLAIR 和 T2 两种模态的输入进行预处理，以去除颅骨并校正偏置场。然后提取病灶和非病灶组织的 3D 补丁，并将其输入到 R-CNN 中。每个 R-CNN 都会生成一个分割结果的概率图，该概率图提供给 ANFIS 以融合结果，从而获得最终的 MS 病灶分割。MS 病灶显示在预处理的 FLAIR 图像上。
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