Bladder segmentation in MRI images using active region growing model

MH2AFormer: An Efficient Multiscale Hierarchical Hybrid Attention With a Transformer for Bladder Wall and Tumor Segmentation

Achieving accurate bladder wall and tumor segmentation from MRI is critical for diagnosing and treating bladder cancer. However, automated segmentation remains challenging due to factors such as comparable density distributions, intricate tumor morphologies, and unclear boundaries. Considering the attributes of bladder MRI images, we propose an efficient multiscale hierarchical hybrid attention with a transformer (MH2AFormer) for bladder cancer and wall segmentation. Specifically, a multiscale hybrid attention and transformer (MHAT) module in the encoder is designed to adaptively extract and aggregate multiscale hybrid feature representations from the input image. In the decoder stage, we devise a multiscale hybrid attention (MHA) module to generate high-quality segmentation results from multiscale hybrid features. Combining these modules enhances the feature representation and guides the model to focus on tumor and wall regions, which helps to solve bladder image segmentation challenges. Moreover, MHAT utilizes the Fast Fourier Transformer with a large kernel (e.g., 224 × 224) to model global feature relationships while reducing computational complexity in the encoding stage. The model performance was evaluated on two datasets. As a result, the model achieves relatively best results regarding the intersection over union (IoU) and dice similarity coefficient (DSC) on both datasets (Dataset A: IoU = 80.26%, DSC = 88.20%; Dataset B: IoU = 89.74%, DSC = 94.48%). These advantageous outcomes substantiate the practical utility of our approach, highlighting its potential to alleviate the workload of radiologists when applied in clinical settings.

Deep learning in bladder cancer imaging: A review

Deep learning (DL) is a rapidly developing field in machine learning (ML). The concept of deep learning originates from research on artificial neural networks and is an upgrade of traditional neural networks. It has achieved great success in various domains and has shown potential in solving medical problems, particularly when using medical images. Bladder cancer (BCa) is the tenth most common cancer in the world. Imaging, as a safe, noninvasive, and relatively inexpensive technique, is a powerful tool to aid in the diagnosis and treatment of bladder cancer. In this review, we provide an overview of the latest progress in the application of deep learning to the imaging assessment of bladder cancer. First, we review the current deep learning approaches used for bladder segmentation. We then provide examples of how deep learning helps in the diagnosis, staging, and treatment management of bladder cancer using medical images. Finally, we summarize the current limitations of deep learning and provide suggestions for future improvements.

Study Progress of Noninvasive Imaging and Radiomics for Decoding the Phenotypes and Recurrence Risk of Bladder Cancer

Urinary bladder cancer (BCa) is a highly prevalent disease among aged males. Precise diagnosis of tumor phenotypes and recurrence risk is of vital importance in the clinical management of BCa. Although imaging modalities such as CT and multiparametric MRI have played an essential role in the noninvasive diagnosis and prognosis of BCa, radiomics has also shown great potential in the precise diagnosis of BCa and preoperative prediction of the recurrence risk. Radiomics-empowered image interpretation can amplify the differences in tumor heterogeneity between different phenotypes, i.e., high-grade vs. low-grade, early-stage vs. advanced-stage, and nonmuscle-invasive vs. muscle-invasive. With a multimodal radiomics strategy, the recurrence risk of BCa can be preoperatively predicted, providing critical information for the clinical decision making. We thus reviewed the rapid progress in the field of medical imaging empowered by the radiomics for decoding the phenotype and recurrence risk of BCa during the past 20 years, summarizing the entire pipeline of the radiomics strategy for the definition of BCa phenotype and recurrence risk including region of interest definition, radiomics feature extraction, tumor phenotype prediction and recurrence risk stratification. We particularly focus on current pitfalls, challenges and opportunities to promote massive clinical applications of radiomics pipeline in the near future.

A Potential Application of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined with Photodynamic Diagnosis for the Detection of Bladder Carcinoma in Situ: Toward the Future 'MRI-PDD Fusion TURBT'

The detection of carcinoma in situ (CIS) is essential for the management of high-risk non-muscle invasive bladder cancers. Here, we focused on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with photodynamic diagnosis (PDD) for the detection of CIS. A total of 45 patients undergoing pre-surgical DCE-MRI and PDD-assisted endoscopic surgery accompanied by biopsies of the eight segmentations were analyzed. Immunohistochemical analysis of the biopsies revealed hypervascularity of CIS lesions, a cause of strong submucosal contrast-enhancement. It was found that 56 (16.2%) of 344 biopsies had pathologically proven CIS. In the DCE-MRI, the overall sensitivity and specificity for detecting CIS were 48.2% and 81.9%, respectively. We set out two different combinations of PDD and DCE-MRI for detecting CIS. Combination 1 was positive when either the PDD or DCE-MRI were test-positive. Combination 2 was positive only when both PDD and DCE-MRI were test-positive. The overall sensitivity of combinations 1 and 2 were 75.0% and 37.5%, respectively (McNemar test, vs PDD alone; p = 0.041 and p < 0.001, respectively). However, the specificity was 74.0% and 91.7%, respectively (vs PDD alone; both p < 0.001). Our future goal is to establish 'MRI-PDD fusion transurethral resction of the bladder tumor (TURBT), which could be an effective therapeutic and diagnostic approach in the clinical management of high-risk disease.

Adaptation and evaluation of the multiple organs OSD for T2 MRI prostate segmentation

This paper deals with the adaptation, the tuning and the evaluation of the multiple organs Optimal Surface Detection (OSD) algorithm for the T2 MRI prostate segmentation. This algorithm is initialized by first surface approximations of the prostate (obtained after a model adjustment), the bladder (obtained automatically) and the rectum (interactive geometrical model). These three organs are then segmented together in a multiple organs OSD scheme which proposes a competition between the gray level characteristics and some topological and anatomical information of these three organs. This method has been evaluated on the MICCAI Grand Challenge: Prostate MR Image Segmentation (PROMISE) 2012 training dataset.

A high resolution bladder wall map: feasibility study

The aim of this work is to provide the surgeon-urologist with a system for automatic 2D and 3D-reconstruction of the bladder wall to help him within the treatment of bladder cancer as well as planning and documentation of the interventions. Within this small pilot-framework a fast feasibility study was made to clear if it is generally possible to build a bladder wall model using a special endoscope with an embedded laser-based distance measurement, an optical navigation system and modern image stitching techniques. Some experiments with a realistic bladder phantom have shown that this initial concept is generally acceptable and can be used with some extensions to build a system which can provide an automatic bladder wall reconstruction in real time to be used within a surgical intervention.