论文标题
拓扑保护细分网络：连接组件的深度学习分割框架
Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component
论文作者
论文摘要
旨在自动提取解剖学或病理结构的医学图像分割在计算机辅助诊断和疾病分析中起关键作用。尽管问题已经得到广泛研究，但现有的方法容易出现拓扑错误。在医学成像中，通常已知结构的拓扑结构，例如肾脏或肺部。在分割过程中保留结构的拓扑对于准确的图像分析至关重要。在这项工作中，提出了一种新颖的基于学习的分割模型。培训一个{\ IT拓扑传播分割网络（TPSN）}，以给出保留规定拓扑的输入图像的准确分割结果。 TPSN是一种基于变形的模型，可通过UNET产生变形图，该图像将医疗图像和模板蒙版作为输入。主要思想是将模板掩码变形，该模板掩码描述了通过差异形态描述规定的拓扑，以分割图像中的对象。模板掩模中形状的拓扑结构在差异图下得到充分保存。地图的差异特性是通过引入与Jacobian相关的损失函数中的正则化项来控制的。因此，可以保证保留拓扑的细分结果。此外，本文开发了多尺度TPSN，其中包含图像的多级信息以产生更精确的分割结果。为了评估我们的方法，我们在HAM10000上应用了2D TPSN，在Kits21上应用了3D TPSN。实验结果说明了我们的方法的表现优于基线UNET分割模型，其中/不使用连接组件分析（CCA）的骰子分数和IOU得分。此外，结果表明，即使在具有挑战性的情况下，我们的方法也可以产生可靠的结果，在这种情况下，UNET和CCA的Pixel细分模型无法获得准确的结果。
Medical image segmentation, which aims to automatically extract anatomical or pathological structures, plays a key role in computer-aided diagnosis and disease analysis. Despite the problem has been widely studied, existing methods are prone to topological errors. In medical imaging, the topology of the structure, such as the kidney or lung, is usually known. Preserving the topology of the structure in the segmentation process is of utmost importance for accurate image analysis. In this work, a novel learning-based segmentation model is proposed. A {\it topology-preserving segmentation network (TPSN)} is trained to give an accurate segmentation result of an input image that preserves the prescribed topology. TPSN is a deformation-based model that yields a deformation map through a UNet, which takes the medical image and a template mask as inputs. The main idea is to deform a template mask describing the prescribed topology by a diffeomorphism to segment the object in the image. The topology of the shape in the template mask is well preserved under the diffeomorphic map. The diffeomorphic property of the map is controlled by introducing a regularization term related to the Jacobian in the loss function. As such, a topology-preserving segmentation result can be guaranteed. Furthermore, a multi-scale TPSN is developed in this paper that incorporates multi-level information of images to produce more precise segmentation results. To evaluate our method, we applied the 2D TPSN on Ham10000 and 3D TPSN on KiTS21. Experimental results illustrate our method outperforms the baseline UNet segmentation model with/without connected-component analysis (CCA) by both the dice score and IoU score. Besides, results show that our method can produce reliable results even in challenging cases, where pixel-wise segmentation models by UNet and CCA fail to obtain accurate results.
