背景:多参数磁共振成像(MP-MRI)中的前列腺癌(PCa)分割景观被分割,在纳入背景细节方面明显缺乏共识,最终导致不一致的分段输出。鉴于PCa的复杂性和异质性,传统的成像分割算法经常失败,促使需要专门的研究和完善。
目的:本研究试图剖析和比较各种分割方法,强调背景信息和腺体掩模在实现优越的PCa分割中的作用。目标是系统地完善分割网络,以确定最有效的方法。
方法:232名患者(年龄61-73岁,前列腺特异性抗原:3.4-45.6ng/mL),他们接受了MP-MRI,然后进行了前列腺活检,被分析。先进的分割模型,即注意-Unet,结合了U-Net和注意门,被用于培训和验证。通过多尺度模块和复合损失函数进一步增强了模型,最终导致了Matt-Unet的发展。性能指标包括骰子相似系数(DSC)和准确度(ACC)。
结果:Matt-Unet模型,整合了背景信息和腺体面具,使用原始图像的性能优于基线U-Net模型,产生显著收益(DSC:0.7215与0.6592;ACC:0.8899vs.0.8601,p<0.001)。
结论:设计了一种有针对性的实用的PCa分割方法,通过结合背景信息和腺体掩模,可以显着改善MP-MRI上的PCa分割。Matt-Unet模型展示了有效描绘PCa的有前途的能力,提高MP-MRI分析的精度。
BACKGROUND: The landscape of prostate cancer (PCa) segmentation within multiparametric magnetic resonance imaging (MP-MRI) was fragmented, with a noticeable lack of consensus on incorporating background details, culminating in inconsistent segmentation outputs. Given the complex and heterogeneous nature of PCa, conventional imaging segmentation algorithms frequently fell short, prompting the need for specialized research and refinement.
OBJECTIVE: This study sought to dissect and compare various segmentation methods, emphasizing the role of background information and gland masks in achieving superior PCa segmentation. The goal was to systematically refine segmentation networks to ascertain the most efficacious approach.
METHODS: A cohort of 232 patients (ages 61-73 years old, prostate-specific antigen: 3.4-45.6 ng/mL), who had undergone MP-MRI followed by prostate biopsies, was analyzed. An advanced segmentation model, namely Attention-Unet, which combines U-Net with attention gates, was employed for training and validation. The model was further enhanced through a multiscale module and a composite loss function, culminating in the development of Matt-Unet. Performance metrics included Dice Similarity Coefficient (DSC) and accuracy (ACC).
RESULTS: The Matt-Unet model, which integrated background information and gland masks, outperformed the baseline U-Net model using raw images, yielding significant gains (DSC: 0.7215 vs. 0.6592; ACC: 0.8899 vs. 0.8601, p < 0.001).
CONCLUSIONS: A targeted and practical PCa segmentation method was designed, which could significantly improve PCa segmentation on MP-MRI by combining background information and gland masks. The Matt-Unet model showcased promising capabilities for effectively delineating PCa, enhancing the precision of MP-MRI analysis.