School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, UP, India.
School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, UP, India.
Comput Biol Med. 2022 Oct;149:106017. doi: 10.1016/j.compbiomed.2022.106017. Epub 2022 Aug 28.
Stroke risk assessment using deep learning (DL) requires automated, accurate, and real-time risk assessment while ensuring compact model size. Previous DL paradigms suffered from challenges like memory size, low speed, and complex in nature lacking multi-ethnic, and multi-institution databases. This research segments and measures the area of the plaque far wall of the common carotid (CCA) and internal carotid arteries (ICA) in B-mode ultrasound using four types of solo, namely, UNet, UNet+, UNet++, and UNet+++, and three types of hybrids, namely, Inception-UNet, Fractal-UNet, and Squeeze-UNet, architectures. These seven models are benchmarked against autoencoder-based solution. Three kinds of databases, namely, CCA, ICA, and combined CCA + ICA were implemented using K5 cross-validation protocol. This was validated using unseen Hong Kong data. The CCA database consisted of 379 Japanese images from low-to medium-risk, while the ICA database consisted of 970 Japanese images taken from 97 medium-to high-risk patients. Using the coefficient of correlation (CC) metric between automated measured area and manually delineated area, seven deep learning solo and hybrid models for CCA yielded 0.96, 0.96, 0.98, 0.95, 0.96, and 0.96 respectively, whereas ICA yielded 0.99, 0.99, 0.98, 0.99, 0.98, 0.98, and 0.98 respectively. Area under the receiver operating characteristics curve values for CCA images was 0.97, 0.969, 0.974, 0.969, 0.962, 0.969, and 0.960 respectively, whereas for ICA images were 0.99, 0.989, 0.988, 0.989, 0.986, 0.989, and 0.988, respectively (p < 0.001). The percentage improvement in offline memory size, training time and training parameters for Squeeze-UNet compared to UNet++ were 569%, 122.46%, and 569%, respectively.
使用深度学习（DL）进行中风风险评估需要自动、准确和实时的风险评估，同时确保模型尺寸紧凑。以前的 DL 范式存在内存大小、速度低和本质上复杂等挑战，缺乏多民族和多机构数据库。本研究使用四种 solo 类型，即 UNet、UNet+、UNet++和 UNet+++，以及三种 hybrid 类型，即 Inception-UNet、Fractal-UNet 和 Squeeze-UNet 架构，对 B 模式超声中的颈总动脉（CCA）和颈内动脉（ICA）的斑块远壁进行分割和测量。这七种模型与基于自动编码器的解决方案进行了基准测试。使用 K5 交叉验证协议实现了三种类型的数据库，即 CCA、ICA 和组合的 CCA+ICA。使用香港未见过的数据进行了验证。CCA 数据库由来自低至中风险的 379 张日本图像组成，而 ICA 数据库由来自 97 名中至高风险患者的 970 张日本图像组成。使用自动测量面积与手动勾画面积之间的相关系数（CC）度量，CCA 的七种深度学习 solo 和 hybrid 模型的结果分别为 0.96、0.96、0.98、0.95、0.96 和 0.96，而 ICA 的结果分别为 0.99、0.99、0.98、0.99、0.98、0.98 和 0.98。CCA 图像的接收器操作特性曲线下面积值分别为 0.97、0.969、0.974、0.969、0.962、0.969 和 0.960，而 ICA 图像的面积值分别为 0.99、0.989、0.988、0.989、0.986、0.989 和 0.988（p<0.001）。与 UNet++相比，Squeeze-UNet 的离线内存大小、训练时间和训练参数的百分比改进分别为 569%、122.46%和 569%。
