Zhang Yuanxi, Deng Xiwen, Li Tingting, Li Yuan, Wang Xiaohui, Lu Man, Yang Lifeng
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Department of Ultrasound, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
J Imaging Inform Med. 2025 Aug;38(4):2229-2240. doi: 10.1007/s10278-024-01358-6. Epub 2024 Dec 11.
Ultrasound imaging is the most cost-effective approach for the early detection of rectal cancer, which is a high-risk cancer. Our goal was to design an effective method that can accurately identify and segment rectal tumours in ultrasound images, thereby facilitating rectal cancer diagnoses for physicians. This would allow physicians to devote more time to determining whether the tumour is benign or malignant and whether it has metastasized rather than merely confirming its presence. Data originated from the Sichuan Province Cancer Hospital. The test, training, and validation sets were composed of 53 patients with 173 images, 195 patients with 1247 images, and 20 patients with 87 images, respectively. We created a deep learning network architecture consisting of encoders and decoders. To enhance global information capture, we substituted traditional convolutional decoders with global attention decoders and incorporated effective channel information fusion for multiscale information integration. The Dice coefficient (DSC) of the proposed model was 75.49%, which was 4.03% greater than that of the benchmark model, and the Hausdorff distance 95(HD95) was 24.75, which was 8.43 lower than that of the benchmark model. The paired t-test statistically confirmed the significance of the difference between our model and the benchmark model, with a p-value less than 0.05. The proposed method effectively identifies and segments rectal tumours of diverse shapes. Furthermore, it distinguishes between normal rectal images and those containing tumours. Therefore, after consultation with physicians, we believe that our method can effectively assist physicians in diagnosing rectal tumours via ultrasound.
超声成像对于早期检测直肠癌（一种高危癌症）而言是最具成本效益的方法。我们的目标是设计一种有效的方法，能够在超声图像中准确识别并分割直肠肿瘤，从而为医生的直肠癌诊断提供便利。这将使医生能够将更多时间用于确定肿瘤是良性还是恶性以及是否已经转移，而不仅仅是确认其存在。数据源自四川省肿瘤医院。测试集、训练集和验证集分别由53名患者的173幅图像、195名患者的1247幅图像以及20名患者的87幅图像组成。我们创建了一个由编码器和解码器组成的深度学习网络架构。为了增强全局信息捕获能力，我们用全局注意力解码器替代了传统卷积解码器，并纳入了有效的通道信息融合以进行多尺度信息整合。所提出模型的骰子系数（DSC）为75.49%，比基准模型高4.03%，豪斯多夫距离95（HD95）为24.75，比基准模型低8.43。配对t检验从统计学上证实了我们的模型与基准模型之间差异的显著性，p值小于0.05。所提出的方法能够有效地识别和分割各种形状的直肠肿瘤。此外，它还能区分正常直肠图像和包含肿瘤图像。因此，在与医生协商后，我们认为我们的方法能够有效地协助医生通过超声诊断直肠肿瘤。
