School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada.
BMC Bioinformatics. 2022 Aug 11;23(1):332. doi: 10.1186/s12859-022-04882-w.
MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to [Formula: see text] compared to the state-of-the-art models) in detecting and segmenting brain tissue images.
MRI 脑图像的对比度通常较低，这使得难以确定脑图像边界处的信息属于哪个区域。这可能会使边界处的特征提取更加具有挑战性，因为这些特征可能会产生误导，因为它们可能混合了不同脑区的特性。因此，为了缓解这个问题，图像边界检测在医学图像分割中起着至关重要的作用，特别是在脑分割中，因为不清晰的边界可能会使脑分割结果恶化。然而，考虑到脑图像的质量较低，脑图像分割中的边界检测仍然具有挑战性。尽管已经投入了大量的研究来改善边界检测和脑分割，但这两个问题是独立解决的，也就是说，很少关注将边界检测应用于脑分割任务。因此，在本文中，我们提出了一种基于边界检测的脑图像分割模型。为此，我们首先设计了一个用于检测和分割脑组织结构图像的边界分割网络。然后，我们设计了一个边界信息模块（BIM）来区分不同的脑组织结构的边界。之后，我们在我们的变压器的编码器输出层添加了一个边界注意力门（BAG），以捕获更多信息丰富的局部细节。我们在两个脑组织结构图像数据集上评估了我们提出的模型，包括婴儿和成人的大脑。我们的模型的广泛评估实验表明，在检测和分割脑组织结构图像方面具有更好的性能（与最先进的模型相比，Dice 系数（DC）精度高达[Formula: see text]）。
