School of information engineering, Huzhou University, Huzhou, Zhejiang, China.
Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural 8 Resources, Huzhou University, Huzhou, China.
Med Phys. 2024 Aug;51(8):5236-5249. doi: 10.1002/mp.17171. Epub 2024 May 20.
BACKGROUND: Bladder prolapse is a common clinical disorder of pelvic floor dysfunction in women, and early diagnosis and treatment can help them recover. Pelvic magnetic resonance imaging (MRI) is one of the most important methods used by physicians to diagnose bladder prolapse; however, it is highly subjective and largely dependent on the clinical experience of physicians. The application of computer-aided diagnostic techniques to achieve a graded diagnosis of bladder prolapse can help improve its accuracy and shorten the learning curve. PURPOSE: The purpose of this study is to combine convolutional neural network (CNN) and vision transformer (ViT) for grading bladder prolapse in place of traditional neural networks, and to incorporate attention mechanisms into mobile vision transformer (MobileViT) for assisting in the grading of bladder prolapse. METHODS: This study focuses on the grading of bladder prolapse in pelvic organs using a combination of a CNN and a ViT. First, this study used MobileNetV2 to extract the local features of the images. Next, a ViT was used to extract the global features by modeling the non-local dependencies at a distance. Finally, a channel attention module (i.e., squeeze-and-excitation network) was used to improve the feature extraction network and enhance its feature representation capability. The final grading of the degree of bladder prolapse was thus achieved. RESULTS: Using pelvic MRI images provided by a Huzhou Maternal and Child Health Care Hospital, this study used the proposed method to grade patients with bladder prolapse. The accuracy, Kappa value, sensitivity, specificity, precision, and area under the curve of our method were 86.34%, 78.27%, 83.75%, 95.43%, 85.70%, and 95.05%, respectively. In comparison with other CNN models, the proposed method performed better. CONCLUSIONS: Thus, the model based on attention mechanisms exhibits better classification performance than existing methods for grading bladder prolapse in pelvic organs, and it can effectively assist physicians in achieving a more accurate bladder prolapse diagnosis.
背景：膀胱膨出是女性盆底功能障碍性疾病的一种常见临床疾病，早期诊断和治疗有助于其康复。盆腔磁共振成像（MRI）是医生诊断膀胱膨出最常用的方法之一，但它高度主观，在很大程度上依赖于医生的临床经验。应用计算机辅助诊断技术对膀胱膨出进行分级诊断，可以帮助提高其准确性，缩短学习曲线。
目的：本研究旨在结合卷积神经网络（CNN）和视觉转换器（ViT）对膀胱膨出进行分级诊断，代替传统神经网络，并在移动视觉转换器（MobileViT）中引入注意力机制，辅助膀胱膨出分级诊断。
方法：本研究重点研究盆腔器官中膀胱膨出的分级，采用 CNN 和 ViT 相结合的方法。首先，本研究使用 MobileNetV2 提取图像的局部特征。然后，使用 ViT 通过建模远距离的非局部依赖关系提取全局特征。最后，使用通道注意力模块（即挤压激励网络）改进特征提取网络，增强其特征表示能力。最后实现膀胱膨出程度的分级。
结果：使用湖州市妇幼保健院提供的盆腔 MRI 图像，本研究使用所提出的方法对患有膀胱膨出的患者进行分级。本方法的准确率、Kappa 值、灵敏度、特异度、精度和曲线下面积分别为 86.34%、78.27%、83.75%、95.43%、85.70%和 95.05%，优于其他 CNN 模型。
结论：因此，基于注意力机制的模型在分级诊断盆腔器官中膀胱膨出方面的分类性能优于现有方法，可以有效帮助医生做出更准确的膀胱膨出诊断。
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