Wei Chao, Liu Zheng, Zhang Yibo, Fan Lianhui
Department of Urology, General Hospital of Northern Theater Command, Shenyang, China.
Department of Graduate School, China Medical University, Shenyang, China.
Digit Health. 2025 Jan 24;11:20552076251314546. doi: 10.1177/20552076251314546. eCollection 2025 Jan-Dec.
Prostate cancer (PCa) is the second most common cancer in males worldwide, requiring improvements in diagnostic imaging to identify and treat it at an early stage. Bi-parametric magnetic resonance imaging (bpMRI) is recognized as an essential diagnostic technique for PCa, providing shorter acquisition times and cost-effectiveness. Nevertheless, accurate diagnosis using bpMRI images is difficult due to the inconspicuous and diverse characteristics of malignant tumors and the intricate structure of the prostate gland. An automated system is required to assist the medical professionals in accurate and early diagnosis with less effort.
This study recognizes the impact of zonal features on the advancement of the disease. The aim is to improve the diagnostic performance through a novel automated approach of a two-step mechanism using bpMRI images. First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. Secondly, pretraining the same type of Attention U-Net is performed for lesion segmentation.
The performance of the pretrained models and training an attention-guided U-Net from the scratch for segmenting tumors on the prostate region is analyzed. The proposed attention-guided U-Net model achieved an area under the curve (AUC) of 0.85 and a dice similarity coefficient value of 0.82, outperforming some other pretrained deep learning models.
Our approach greatly enhances the identification and categorization of clinically significant PCa by including zonal data. Our approach exhibits exceptional performance in the accurate segmentation of bpMRI images compared to current techniques, as evidenced by thorough validation of a diverse dataset. This research not only enhances the field of medical imaging for oncology but also underscores the potential of deep learning models to progress PCa diagnosis and personalized patient care.
前列腺癌（PCa）是全球男性中第二常见的癌症，需要改进诊断成像技术以便在早期阶段识别和治疗该疾病。双参数磁共振成像（bpMRI）被认为是PCa的一项重要诊断技术，它具有采集时间短和成本效益高的特点。然而，由于恶性肿瘤特征不明显且多样，以及前列腺腺体结构复杂，使用bpMRI图像进行准确诊断较为困难。因此需要一个自动化系统来协助医学专业人员更轻松地进行准确和早期诊断。
本研究认识到区域特征对疾病进展的影响。目的是通过一种使用bpMRI图像的新颖的两步机制自动化方法来提高诊断性能。首先，预训练一个基于卷积神经网络（CNN）的注意力引导U-Net模型，用于在前列腺区域分割感兴趣区域。其次，对同一类型的注意力U-Net进行预训练以进行病变分割。
分析了预训练模型的性能以及从零开始训练一个注意力引导U-Net用于分割前列腺区域肿瘤的情况。所提出的注意力引导U-Net模型的曲线下面积（AUC）为0.85，骰子相似系数值为0.82，优于其他一些预训练的深度学习模型。
我们的方法通过纳入区域数据极大地增强了对具有临床意义的PCa的识别和分类。与当前技术相比，我们的方法在bpMRI图像的准确分割方面表现出卓越性能，这在对多样数据集的全面验证中得到了证明。这项研究不仅推动了肿瘤学医学成像领域的发展，还强调了深度学习模型在推进PCa诊断和个性化患者护理方面的潜力。
