MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons.

Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tumor. Hence, multiregion segmentation on patients presenting with symptoms of bladder tumors using deep learning heralds a new level of staging accuracy and prediction of the biologic behavior of the tumor. Nevertheless, despite the success of these models in other medical problems, progress in multiregion bladder segmentation, particularly in MRI and CT modalities, is still at a nascent stage, with just a handful of works tackling a multiregion scenario. Furthermore, most existing approaches systematically follow prior literature in other clinical problems, without casting a doubt on the validity of these methods on bladder segmentation, which may present different challenges. Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted.

Fully Automated Segmentation of Bladder Sac and Measurement of Detrusor Wall Thickness from Transabdominal Ultrasound Images.

Ultrasound measurements of detrusor muscle thickness have been proposed as a diagnostic biomarker in patients with bladder overactivity and voiding dysfunction. In this study, we present an approach based on deep learning (DL) and dynamic programming (DP) to segment the bladder sac and measure the detrusor muscle thickness from transabdominal 2D B-mode ultrasound images. To assess the performance of our method, we compared the results of automated methods to the manually obtained reference bladder segmentations and wall thickness measurements of 80 images obtained from 11 volunteers. It takes less than a second to segment the bladder from a 2D B-mode image for the DL method. The average Dice index for the bladder segmentation is 0.93 ± 0.04 mm, and the average root-mean-square-error and standard deviation for wall thickness measurement are 0.7 ± 0.2 mm, which is comparable to the manual ground truth. The proposed fully automated and fast method could be a useful tool for segmentation and wall thickness measurement of the bladder from transabdominal B-mode images. The computation speed and accuracy of the proposed method will enable adaptive adjustment of the ultrasound focus point, and continuous assessment of the bladder wall during the filling and voiding process of the bladder.

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks.

PURPOSE: Precise segmentation of bladder walls and tumor regions is an essential step toward noninvasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC). However, the automatic delineation of bladder walls and tumor in magnetic resonance images (MRI) is a challenging task, due to important bladder shape variations, strong intensity inhomogeneity in urine, and very high variability across the population, particularly on tumors' appearance. To tackle these issues, we propose to leverage the representation capacity of deep fully convolutional neural networks. METHODS: The proposed network includes dilated convolutions to increase the receptive field without incurring extra cost or degrading its performance. Furthermore, we introduce progressive dilations in each convolutional block, thereby enabling extensive receptive fields without the need for large dilation rates. The proposed network is evaluated on 3.0T T2-weighted MRI scans from 60 pathologically confirmed patients with BC. RESULTS: Experiments show the proposed model to achieve a higher level of accuracy than state-of-the-art methods, with a mean Dice similarity coefficient of 0.98, 0.84, and 0.69 for inner wall, outer wall, and tumor region segmentation, respectively. These results represent a strong agreement with reference contours and an increase in performance compared to existing methods. In addition, inference times are less than a second for a whole three-dimensional (3D) volume, which is between two and three orders of magnitude faster than related state-of-the-art methods for this application. CONCLUSION: We showed that a CNN can yield precise segmentation of bladder walls and tumors in BC patients on MRI. The whole segmentation process is fully automatic and yields results similar to the reference standard, demonstrating the viability of deep learning models for the automatic multiregion segmentation of bladder cancer MRI images.

A Review on Virtual Cystoscopy Techniques / 中国医学物理学杂志

Objective: Bladder cancer is the ninth cause of cancer deaths and has high recurrence rate after resection of the tumors. Cystoscopy is the current most accurate method for investigating the bladder abnormalities. However, it is expensive, uncomfortable and invasive. It is possible to induce bleeding, urinary-tract infection and even puncture of bladder. Advances in medical imaging and computer technologies make virtual cystoscopy a potential alternative. Methods: Computed tomography (CT) and magnetic resonance imaging (MR/) are the preferred imaging modalities for virtual cystoseopy to get clear structural images or (and) functional images of the bladder. The boundary of bladder is segmented manually or automatically, and then the bladder is reconstructed and displayed by surface rendering. Useful features are extracted from the images and expressed o nthe reconstructed bladder to provide more valuable diagnosis information for doctors. Results: Comparing with conventional cystuscopy, virtual cystuscopy is noninvasive, more convenient, flexible and can provide more useful diagnosis information as well. Conclusions: Virtual cystoscopy is a promising method of detection and reexamination of bladder cancer. So far, more researches are needed for the virtual cystoscopy before clinical application. It clinical and commercial value are under investigation.