Background: Bladder cancer (BC) segmentation on MRI images is the first step to determining the presence of muscular invasion. This study aimed to assess the tumor segmentation performance of three deep learning (DL) models on multi-parametric MRI (mp-MRI) images. Methods: We studied 53 patients with bladder cancer. Bladder tumors were segmented on each slice of T2-weighted (T2WI), diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted contrast-enhanced (T1WI) images acquired at a 3Tesla MRI scanner. We trained Unet, MAnet, and PSPnet using three loss functions: cross-entropy (CE), dice similarity coefficient loss (DSC), and focal loss (FL). We evaluated the model performances using DSC, Hausdorff distance (HD), and expected calibration error (ECE). Results: The MAnet algorithm with the CE+DSC loss function gave the highest DSC values on the ADC, T2WI, and T1WI images. PSPnet with CE+DSC obtained the smallest HDs on the ADC, T2WI, and T1WI images. The segmentation accuracy overall was better on the ADC and T1WI than on the T2WI. The ECEs were the smallest for PSPnet with FL on the ADC images, while they were the smallest for MAnet with CE+DSC on the T2WI and T1WI. Conclusions: Compared to Unet, MAnet and PSPnet with a hybrid CE+DSC loss function displayed better performances in BC segmentation depending on the choice of the evaluation metric.
背景：磁共振成像（MRI）图像中的膀胱癌（BC）分割是判断是否存在肌层浸润的首要步骤。本研究旨在评估三种深度学习（DL）模型在多参数磁共振（mp-MRI）图像上的肿瘤分割性能。方法：我们研究了53例膀胱癌患者。在3特斯拉MRI扫描仪获取的T2加权（T2WI）、扩散加权成像/表观扩散系数（DWI/ADC）及T1加权对比增强（T1WI）图像的每一层面进行膀胱肿瘤分割。我们使用交叉熵（CE）、戴斯相似系数损失（DSC）和焦点损失（FL）三种损失函数分别训练了Unet、MAnet和PSPnet模型。通过DSC、豪斯多夫距离（HD）和预期校准误差（ECE）指标评估模型性能。结果：采用CE+DSC损失函数的MAnet算法在ADC、T2WI和T1WI图像上获得最高的DSC值；采用CE+DSC的PSPnet在ADC、T2WI和T1WI图像上取得最小的HD值。总体而言，ADC和T1WI图像的分割精度优于T2WI图像。在ADC图像上，采用FL的PSPnet获得最小的ECE值；而在T2WI和T1WI图像上，采用CE+DSC的MAnet获得最小的ECE值。结论：相较于Unet，采用混合CE+DSC损失函数的MAnet和PSPnet在膀胱癌分割中表现出更优的性能，具体效果取决于评估指标的选择。
Deep Learning Algorithms for Bladder Cancer Segmentation on Multi-Parametric MRI
肿瘤(癌症)患者之家