Proceedings Article | 7 April 2023
KEYWORDS: Image segmentation, Bladder cancer, Education and training, Bladder, Performance modeling, 3D modeling, Deep learning, Cancer, Tumor growth modeling, Contour modeling
We are developing a decision support system for treatment response assessment of bladder cancer in CT urography (CTU). Accurate segmentation of bladder cancer is a critical and challenging task. We previously developed a bladder cancer segmentation method using a deep learning convolutional neural network and level set (DL-CNN+LS) approach. In this study we investigated the application of a U-Net based deep learning (U-Net) model for bladder cancer segmentation. Our new U-Net method did not require the second-stage level set refinement, greatly simplifying the overall segmentation pipeline. The proposed U-Net model utilized a user-defined box to direct the attention of the U-Net to the lesion region by masking out the structured background outside the box. We trained and evaluated the performance of the U-Net segmentations by using hand-drawn 3D contours from a radiologist as reference standard. The segmentation accuracy was evaluated by the average volume intersection ratio (AVI), average percent volume error (AVE), average absolute volume error (AAVE), average minimum distance (AMD), and the Jaccard index (JI). On the validation set, the cropped U-Net achieved values of AVI = 65.5±20.2%, AVE = 3.7±44.4%, AAVE = 30.9±30.8%, AMD = 2.9±1.5 mm, and JI = 51.4±16.8%. The previous DL-CNN+LS approach achieved values of AVI = 36.2±28.4%, AVE = 51.1±39.3%, AAVE = 52.0±38.0%, AMD = 4.1±1.7 mm, and JI = 32.9±27.2%. On the independent test set, the cropped U-Net achieved values of AVI = 66.0±22.8%, AVE = -3.7±39.4%, AAVE = 28.1±27.3%, AMD = 4.5±3.3 mm, and JI = 49.2±20.0%. The DL-CNN+LS achieved values of AVI = 31.2±24.3%, AVE = 59.9±30.9%, AAVE = 60.8±29.0%, AMD = 5.5±2.2 mm, and JI = 27.9±20.8%, respectively. The results demonstrated that the U-Net model could achieve a higher accuracy than the previous DL-CNN+LS model while reducing the complexity of the segmentation pipeline.
