WCEbleedGen: A wireless capsule endoscopy dataset and its benchmarking for automatic bleeding classification, detection, and segmentation
Danube Private University（多瑙河私立大学）；Guru Gobind Singh Indraprastha University（古鲁戈宾德辛德拉普拉斯塔大学）；Indira Gandhi Delhi Technical University for Women（英迪拉甘地德里女子技术大学）；All India Institute of Medical Sciences Delhi（全印度医学科学研究所德里分院）
无线胶囊内窥镜（WCE）的计算机辅助分析至关重要。然而，目前缺乏用于训练和评估出血与非出血帧自动分类、检测和分割的医学标注WCE数据集。本研究致力于开发一个名为WCEbleedGen的医学标注WCE数据集，用于出血与非出血帧的自动分类、检测和分割。它包含2,618帧WCE出血和非出血图像，这些图像收集自各种互联网资源和现有的WCE数据集。使用九个基于分类、三个基于检测和三个基于分割的深度学习模型，对所开发的数据集进行了全面的基准测试和评估。该数据集具有高质量、类别平衡的特点，并包含单一和多个出血点。总体而言，我们的标准基准测试结果表明，Visual Geometric Group (VGG) 19、You Only Look Once version 8 nano (YOLOv8n)和Link network (Linknet)分别在自动分类、检测和基于分割的评估中表现最佳。自动出血诊断对于WCE视频解读至关重要。这一多样化数据集将有助于开发基于实时、多任务学习的创新解决方案，以实现WCE中的自动出血诊断。数据集和代码已公开于https://zenodo.org/records/10156571和https://github.com/misahub2023/Benchmarking-Codes-of-the-WCEBleedGen-dataset。
英文摘要：
Computer-based analysis of Wireless Capsule Endoscopy (WCE) is crucial. However, a medically annotated WCE dataset for training and evaluation of automatic classification, detection, and segmentation of bleeding and non-bleeding frames is currently lacking. The present work focused on development of a medically annotated WCE dataset called WCEbleedGen for automatic classification, detection, and segmentation of bleeding and non-bleeding frames. It comprises 2,618 WCE bleeding and non-bleeding frames which were collected from various internet resources and existing WCE datasets. A comprehensive benchmarking and evaluation of the developed dataset was done using nine classification-based, three detection-based, and three segmentation-based deep learning models. The dataset is of high-quality, is class-balanced and contains single and multiple bleeding sites. Overall, our standard benchmark results show that Visual Geometric Group (VGG) 19, You Only Look Once version 8 nano (YOLOv8n), and Link network (Linknet) performed best in automatic classification, detection, and segmentation-based evaluations, respectively. Automatic bleeding diagnosis is crucial for WCE video interpretations. This diverse dataset will aid in developing of real-time, multi-task learning-based innovative solutions for automatic bleeding diagnosis in WCE. The dataset and code are publicly available at https://zenodo.org/records/10156571 and https://github.com/misahub2023/Benchmarking-Codes-of-the-WCEBleedGen-dataset.
Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X
Indian Institute of Technology, Indore (IITI)（印度理工学院印多尔分校）；Thapar Institute of Engineering & Technology (TIET)（塔帕尔工程技术学院）；Vellore Institute of Technology, Chennai (VIT)（韦洛尔理工学院 Chennai 校区）
PDPM Indian Institute of Information Technology, Design and Manufacturing（PDPM印度信息技术设计与制造学院）
Wireless Capsule Endoscopy is one of the most advanced non-invasive methods for the examination of gastrointestinal tracts. An intelligent computer-aided diagnostic system for detecting gastrointestinal abnormalities like polyp, bleeding, inflammation, etc. is highly exigent in wireless capsule endoscopy image analysis. Abnormalities greatly differ in their shape, size, color, and texture, and some appear to be visually similar to normal regions. This poses a challenge in designing a binary classifier due to intra-class variations. In this study, a hybrid convolutional neural network is propos…
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Wireless capsule endoscopy (WCE) is an effective means of diagnosis of gastrointestinal disorders. Detection of informative scenes by WCE could reduce the length of transmitted videos and can help with the diagnosis. In this paper we propose a simple and efficient method for segmentation of the bleeding regions in WCE captured images. Suitable color channels are selected and classified by a multi-layer perceptron (MLP) structure. The MLP structure is quantized such that the implementation does not require multiplications. The proposed method is tested by simulation on WCE bleeding image datase…
Wireless Capsule Endoscopy (WCE) is device to detect abnormalities in colon,esophagus,small intestinal and stomach, to distinguish bleeding in WCE images from non bleeding is a hard job by human reviewing and very time consuming. Consequently, automation for classifying bleeding frames not only will expedite the process but will reduce the burden on the doctors. Using the purity of the red color we can detect the Bleeding areas in WCE images. But, we could find various intensity of red color values in different parts of the small intestinal,so it is not enough to depend on the red color featur…
Isfahan University of Technology（伊斯法罕理工大学）；Seattle University（西雅图大学）；University of Michigan（密歇根大学）
Wireless capsule endoscopy (WCE) is an effective mean for diagnosis of gastrointestinal disorders. Detection of informative scenes in WCE video could reduce the length of transmitted videos and help the diagnosis procedure. In this paper, we investigate the problem of simplification of neural networks for automatic bleeding region detection inside capsule endoscopy device. Suitable color channels are selected as neural networks inputs, and image classification is conducted using a multi-layer perceptron (MLP) and a convolutional neural network (CNN) separately. Both CNN and MLP structures are …
Temple University（天普大学）；Thomas Jefferson Hospital（托马斯·杰斐逊医院）
Studying animal locomotion improves our understanding of motor control and aids in the treatment of motor impairment. Mice are a premier model of human disease and are the model system of choice for much of basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time-consuming and impossible. Therefore, an automated method is necessary. We propose a method to track the paws of the animal in the following manner: first, segmenting all the possible paws based on color; second, classify…
MAGE: Color-Invariant and Spatial Knowledge Distillation for Gastric Neoplasm Classification
Korea University（韩国大学）；MEDAI；Seoul National University（首尔国立大学）；Vietnam National University, Hanoi（越南河内国家大学）；The Catholic University of Korea, Seoul ST. Mary’s Hospital（韩国天主教大学首尔圣母医院）；Gachon University（加图立大学）
Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field…
Wireless Capsule Endoscopy (WCE) is relatively a new technology to examine the entire GI trace. During an examination, it captures more than 55,000 frames. Reviewing all these images is time-consuming and prone to human error. It has been a challenge to develop intelligent methods assisting physicians to review the frames. The WCE frames are captured in 8-bit color depths which provides enough a color range to detect abnormalities. Here, superpixel based methods are proposed to segment five diseases including: bleeding, Crohn's disease, Lymphangiectasia, Xanthoma, and Lymphoid hyperplasia. Two…
Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations
National Institute of Technology Puducherry（本地治里国家技术学院）；Sardar Vallabhbhai National Institute of Technology Surat（苏拉特萨达尔·瓦拉巴伊国家技术学院）；Norwegian University of Science and Technology（挪威科技大学）
Indian Statistical Institute（印度统计研究所）；Indira Gandhi National Open University（英迪拉·甘地国立开放大学）；Feinberg School of Medicine, Northwestern University（西北大学范伯格医学院）
Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve the anomaly detection rate, reduce physicians' time for screening, and aid in real-world clinical analysis. CADx classification system for video capsule endoscopy has shown a great promise for further improvement. For example, detection of cancerous polyp and bleeding can lead to swift medical response and improve the survival rate of the patients. To this end, an automated CADx system must have high throughput and …
University of Barcelona（巴塞罗那大学）；University Hospital Vall d’Hebron（希伯伦谷大学医院）
Wireless capsule endoscopy is a medical procedure used to visualize the entire gastrointestinal tract and to diagnose intestinal conditions, such as polyps or bleeding. Current analyses are performed by manually inspecting nearly each one of the frames of the video, a tedious and error-prone task. Automatic image analysis methods can be used to reduce the time needed for physicians to evaluate a capsule endoscopy video, however these methods are still in a research phase. In this paper we focus on computer-aided polyp detection in capsule endoscopy images. This is a challenging problem because…
