Inflammatory bowel disease (IBD) is a recurrent bowel disease that usually requires magnetic resonance enterography (MRE) for diagnosis and monitoring. However, recognition of bowel segments from MRE images by a radiologist is challenging and time-consuming. Deep learning-based medical image segmentation has shown the potential to reduce manual effort and provide automated tools to assist in disease management; however, it requires a large-scale fine-annotated dataset for training. To address this gap, we collected MRE data, including HASTE(half-Fourier acquisition single-shot turbo spin-echo) sequences with coronal orientation, from 114 patients with IBD. The bowel images per patient were contoured and annotated into ten segments (stomach, duodenum, small intestine, appendix, cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum), with fine pixel-level annotations labeled by experienced radiologists. Furthermore, we validated the efficiency of several state-of-the-art segmentation methods using this dataset. This study established a high-quality, publicly available whole-bowel segment MR dataset with benchmark results and laid the groundwork for AI research on IBD.

Inflammatory bowel disease (IBD) is a recurrent bowel disease that usually requires magnetic resonance enterography (MRE) for diagnosis and monitoring. However, recognition of bowel segments from MRE images by a radiologist is challenging and time-consuming. Deep learning-based medical image segmentation has shown the potential to reduce manual effort and provide automated tools to assist in disease management; however, it requires a large-scale fine-annotated dataset for training. To address this gap, we collected MRE data, including HASTE(half-Fourier acquisition single-shot turbo spin-echo) sequences with coronal orientation, from 114 patients with IBD. The bowel images per patient were contoured and annotated into ten segments (stomach, duodenum, small intestine, appendix, cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum), with fine pixel-level annotations labeled by experienced radiologists. Furthermore, we validated the efficiency of several state-of-the-art segmentation methods using this dataset. This study established a high-quality, publicly available whole-bowel segment MR dataset with benchmark results and laid the groundwork for AI research on IBD.

Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations employ data from diverse medical facilities to train models for BCa, thereby posing a potential risk of leaking patients' privacy. Ensuring the privacy of patients during the training of machine learning algorithms is a vital consideration that deserves significant attention. Federated learning (FL) is an emerging machine learning paradigm that enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multi-center BCa magnetic resonance imaging (MRI) dataset,  aimed at evaluating the baseline performance of FL. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and fine pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.

Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations employ data from diverse medical facilities to train models for BCa, thereby posing a potential risk of leaking patients' privacy. Ensuring the privacy of patients during the training of machine learning algorithms is a vital consideration that deserves significant attention. Federated learning (FL) is an emerging machine learning paradigm that enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multi-center BCa magnetic resonance imaging (MRI) dataset,  aimed at evaluating the baseline performance of FL. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and fine pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.

Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations employ data from diverse medical facilities to train models for BCa, thereby posing a potential risk of leaking patients' privacy. Ensuring the privacy of patients during the training of machine learning algorithms is a vital consideration that deserves significant attention. Federated learning (FL) is an emerging machine learning paradigm that enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multi-center BCa magnetic resonance imaging (MRI) dataset,  aimed at evaluating the baseline performance of FL. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and fine pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.