Ji Lingyu, Jiang Xiaoyan, Gao Yongbin, Fang Zhijun, Cai Qingping, Wei Ziran
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China.
Changzheng Hospital, Shanghai, 200003, China.
Med Phys. 2020 Sep;47(9):4254-4264. doi: 10.1002/mp.14364. Epub 2020 Aug 2.
Medical image segmentation is an essential component of medical image analysis. Accurate segmentation can assist doctors in diagnosis and relieve their fatigue. Although several image segmentation methods based on U-Net have been proposed, their performances have been observed to be suboptimal in the case of small-sized objects. To address this shortcoming, a novel network architecture is proposed in this study to enhance segmentation performance on small medical targets.
In this paper, we propose a joint multi-scale context attention network architecture to simultaneously capture higher level semantic information and spatial information. In order to obtain a greater number of feature maps during decoding, the network concatenates the images of side inputs by down-sampling during the encoding phase. In the bottleneck layer of the network, dense atrous convolution (DAC) and multi-scale residual pyramid pooling (RMP) modules are exploited to better capture high-level semantic information and spatial information. To improve the segmentation performance on small targets, the attention gate (AG) block is used to effectively suppress feature activation in uncorrelated regions and highlight the target area.
The proposed model is first evaluated on the public dataset DRIVE, on which it performs significantly better than the basic framework in terms of sensitivity (SE), intersection-over-union (IOU), and area under the receiver operating characteristic curve (AUC). In particular, the SE and IOU are observed to increase by 7.46% and 5.97%, respectively. Further, the evaluation indices exhibit improvements compared to those of state-of-the-art methods as well, with SE and IOU increasing by 3.58% and 3.26%, respectively. Additionally, in order to demonstrate the generalizability of the proposed architecture, we evaluate our model on three other challenging datasets. The respective performances are observed to be better than those of state-of-the-art network architectures on the same datasets. Moreover, we use lung segmentation as a comparative experiment to demonstrate the transferability of the advantageous properties of the proposed approach in the context of small target segmentation to the segmentation of large targets. Finally, an ablation study is conducted to investigate the individual contributions of the AG block, the DAC block, and the RMP block to the performance of the network.
The proposed method is evaluated on various datasets. Experimental results demonstrate that the proposed model performs better than state-of-the-art methods in medical image segmentation of small targets.
医学图像分割是医学图像分析的重要组成部分。准确的分割可以辅助医生进行诊断并减轻他们的工作负担。尽管已经提出了几种基于U-Net的图像分割方法，但在处理小尺寸目标时，其性能仍被认为不够理想。为了解决这一缺点，本研究提出了一种新颖的网络架构，以提高对小型医学目标的分割性能。
在本文中，我们提出了一种联合多尺度上下文注意力网络架构，以同时捕获更高层次的语义信息和空间信息。为了在解码过程中获得更多的特征图，该网络在编码阶段通过下采样将侧输入图像进行拼接。在网络的瓶颈层，利用密集空洞卷积（DAC）和多尺度残差金字塔池化（RMP）模块来更好地捕获高级语义信息和空间信息。为了提高对小目标的分割性能，使用注意力门（AG）块来有效抑制不相关区域的特征激活，并突出目标区域。
所提出的模型首先在公共数据集DRIVE上进行评估，在灵敏度（SE）、交并比（IOU）和接收器操作特征曲线下面积（AUC）方面，其表现明显优于基本框架。特别是，SE和IOU分别提高了7.46%和5.97%。此外，与现有方法相比，评估指标也有所改进，SE和IOU分别提高了3.58%和3.26%。此外，为了证明所提出架构的通用性，我们在其他三个具有挑战性的数据集上评估了我们的模型。在相同数据集上，各自的性能均优于现有网络架构。此外，我们以肺部分割作为对比实验，以证明所提出方法在小目标分割背景下的优势特性对大目标分割的可转移性。最后，进行了消融研究，以探究AG块、DAC块和RMP块对网络性能的各自贡献。
所提出的方法在各种数据集上进行了评估。实验结果表明，所提出的模型在小型目标的医学图像分割中表现优于现有方法。
