Background/Objectives: Cystoscopy is necessary for diagnosing bladder cancer, but it has limitations in identifying ambiguous lesions, such as carcinoma in situ (CIS), which leads to a high recurrence rate of bladder cancer. With the significant advancements in deep learning in the medical field, several studies have explored its application in cystoscopy. This study aimed to utilize the VGG19 and Deeplab v3+ deep learning models to classify and segment cystoscope images, respectively. Methods: We classified cystoscope images obtained from 772 patients based on morphology (normal, papillary, flat, mixed) and biopsy results (normal, Ta, T1, T2, CIS, etc.). Experienced urologists annotated and labeled the lesion areas and image categories. The classification model for bladder cancer lesion, annotated with pathological results, was developed using VGG19 with an additional fully connected layer, utilizing sparse categorical cross-entropy as the loss function. The Deeplab v3+ model was used for segmenting each morphological type of bladder cancer in the cystoscope images, employing the dice coefficient loss function. The classification model was evaluated using validation accuracy and correlation with biopsy results, while the segmentation model was assessed using the Intersection over Union (IoU) combined with binary accuracy. Results: The dataset was split into training and validation sets with a 4:1 ratio. The VGG19 classification model achieved an accuracy score of 0.912. The Deeplab v3+ segmentation model achieved an IoU of 0.833 and a binary accuracy of 0.951. Visual analysis revealed a high similarity between the lesions identified by Deeplab v3+ and those labeled by experts. Conclusions: In this study, we applied two deep learning models using well-annotated datasets of cystoscopic images. Both VGG19 and Deeplab v3+ demonstrated high performance in classification and segmentation, respectively. These models can serve as valuable tools for bladder cancer research and may aid in the diagnosis of bladder cancer.
背景/目的：膀胱镜检查是诊断膀胱癌的必要手段，但其在识别不明确病变（如原位癌）方面存在局限性，导致膀胱癌复发率较高。随着深度学习在医学领域的显著进展，多项研究已探索其在膀胱镜中的应用。本研究旨在分别利用VGG19和Deeplab v3+深度学习模型对膀胱镜图像进行分类和分割。方法：我们基于形态学（正常、乳头状、平坦型、混合型）和活检结果（正常、Ta期、T1期、T2期、原位癌等）对772例患者的膀胱镜图像进行分类。经验丰富的泌尿科医师对病变区域和图像类别进行标注。采用VGG19模型并增加全连接层，以稀疏分类交叉熵作为损失函数，开发了基于病理结果标注的膀胱癌病变分类模型。Deeplab v3+模型则用于分割膀胱镜图像中各类形态的膀胱癌，采用Dice系数损失函数。分类模型通过验证准确率及与活检结果的相关性进行评估，分割模型则通过交并比结合二元准确率进行评估。结果：数据集按4:1比例划分为训练集和验证集。VGG19分类模型的准确率达到0.912。Deeplab v3+分割模型的交并比为0.833，二元准确率为0.951。可视化分析显示，Deeplab v3+识别的病变区域与专家标注结果高度相似。结论：本研究应用两种深度学习模型，基于高质量标注的膀胱镜图像数据集展开分析。VGG19和Deeplab v3+分别在分类和分割任务中表现出优异性能。这些模型可作为膀胱癌研究的重要工具，并有望辅助膀胱癌的临床诊断。
Artificial Intelligence-Based Classification and Segmentation of Bladder Cancer in Cystoscope Images
肿瘤(癌症)患者之家