Shi Zengqiang, Zhang Feifei, Zhang Xiong, Pan Ru, Cheng Yabao, Song Huang, Kang Qiwei, Guo Jianbo, Peng Xin, Li Yulin
Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China.
School of Computer Science, Guangdong University of Education, Guangzhou, 510000, China.
J Imaging Inform Med. 2025 Mar 4. doi: 10.1007/s10278-025-01464-z.
Effective segmentation of cervical cancer tissue from magnetic resonance (MR) images is crucial for automatic detection, staging, and treatment planning of cervical cancer. This study develops an innovative deep learning model to enhance the automatic segmentation of cervical cancer lesions. We obtained 4063 T2WI small-field sagittal, coronal, and oblique axial images from 222 patients with pathologically confirmed cervical cancer. Using this dataset, we employed a convolutional neural network (CNN) along with TransUnet models for segmentation training and evaluation of cervical cancer tissues. In this approach, CNNs are leveraged to extract local information from MR images, whereas Transformers capture long-range dependencies related to shape and structural information, which are critical for precise segmentation. Furthermore, we developed three distinct segmentation models based on coronal, axial, and sagittal T2WI within a small field of view using multidirectional MRI techniques. The dice similarity coefficient (DSC) and mean Hausdorff distance (AHD) were used to assess the performance of the models in terms of segmentation accuracy. The average DSC and AHD values obtained using the TransUnet model were 0.7628 and 0.8687, respectively, surpassing those obtained using the U-Net model by margins of 0.0033 and 0.3479, respectively. The proposed TransUnet segmentation model significantly enhances the accuracy of cervical cancer tissue delineation compared to alternative models, demonstrating superior performance in overall segmentation efficacy. This methodology can improve clinical diagnostic efficiency as an automated image analysis tool tailored for cervical cancer diagnosis.
从磁共振（MR）图像中有效分割宫颈癌组织对于宫颈癌的自动检测、分期和治疗规划至关重要。本研究开发了一种创新的深度学习模型，以增强宫颈癌病变的自动分割。我们从222例经病理证实的宫颈癌患者中获取了4063张T2WI小视野矢状位、冠状位和斜轴位图像。利用该数据集，我们采用卷积神经网络（CNN）和TransUnet模型对宫颈癌组织进行分割训练和评估。在这种方法中，CNN用于从MR图像中提取局部信息，而Transformer捕捉与形状和结构信息相关的长程依赖性，这对于精确分割至关重要。此外，我们使用多方向MRI技术在小视野内基于冠状位、轴位和矢状位T2WI开发了三种不同的分割模型。采用骰子相似系数（DSC）和平均豪斯多夫距离（AHD）来评估模型在分割准确性方面的性能。使用TransUnet模型获得的平均DSC和AHD值分别为0.7628和0.8687，分别比使用U-Net模型获得的值高出0.0033和0.3479。与替代模型相比，所提出的TransUnet分割模型显著提高了宫颈癌组织描绘的准确性，在整体分割效果方面表现出卓越的性能。作为一种专为宫颈癌诊断量身定制的自动化图像分析工具，该方法可以提高临床诊断效率。
