Automatic colorectal polyp detection in colonoscopy video is a fundamental task, which has received a lot of attention. Manually annotating polyp region in a large scale video dataset is time-consuming and expensive, which limits the development of deep learning techniques. A compromise is to train the target model by using labeled images and infer on colonoscopy videos. However, there are several issues between the image-based training and video-based inference, including domain differences, lack of positive samples, and temporal smoothness. To address these issues, we propose an Image-video-joint polyp detection network (Ivy-Net) to address the domain gap between colonoscopy images from historical medical reports and real-time videos. In our Ivy-Net, a modified mixup is utilized to generate training data by combining the positive images and negative video frames at the pixel level, which could learn the domain adaptive representations and augment the positive samples. Simultaneously, a temporal coherence regularization (TCR) is proposed to introduce the smooth constraint on feature-level in adjacent frames and improve polyp detection by unlabeled colonoscopy videos. For evaluation, a new large colonoscopy polyp dataset is collected, which contains 3056 images from historical medical reports of 889 positive patients and 7.5-hour videos of 69 patients (28 positive). The experiments on the collected dataset demonstrate that our Ivy-Net achieves the state-of-the-art result on colonoscopy video.
University of Bergen（卑尔根大学）；SimulaMet（西穆拉梅特）；Nepal Medical College（尼泊尔医学院）；Kathmandu University（加德满都大学）；UiT The Arctic University of Norway（挪威北极大学）；University of Oslo（奥斯陆大学）；Oslo Metropolitan University（奥斯陆城市大学）
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' p…
Indira Gandhi National Open University（英迪拉·甘地国立开放大学）
Colorectal cancer is one of fatal cancer worldwide. Colonoscopy is the standard treatment for examination, localization, and removal of colorectal polyps. However, it has been shown that the miss-rate of colorectal polyps during colonoscopy is between 6 to 27%. The use of an automated, accurate, and real-time polyp segmentation during colonoscopy examinations can help the clinicians to eliminate missing lesions and prevent further progression of colorectal cancer. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build a fast segmenta…
The University of Kansas（堪萨斯大学）；The University of Kansas Medical Center（堪萨斯大学医学中心）；Washington University School of Medicine（华盛顿大学医学院）；Ryerson University（瑞尔森大学）
Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to trai…
Shenzhen Research Institute of Big Data（深圳大数据研究院）；Sun Yat-sen University（中山大学）；The Chinese University of Hong Kong(Shenzhen)（香港中文大学（深圳））；Longgang District People’s Hospital of Shenzhen（深圳市龙岗区人民医院）
Differentiation of colorectal polyps is an important clinical examination. A computer-aided diagnosis system is required to assist accurate diagnosis from colonoscopy images. Most previous studies at-tempt to develop models for polyp differentiation using Narrow-Band Imaging (NBI) or other enhanced images. However, the wide range of these models' applications for clinical work has been limited by the lagging of imaging techniques. Thus, we propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification (CPC) through directly using white-light …
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
Northwestern University（西北大学）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）；The University of Gothenburg（哥德堡大学）；Harvard University（哈佛大学）；University Hospital Würzburg（维尔茨堡大学医院）；Hanoi Medical University（河内医科大学）；The Institute of Gastroenterology and Hepatology, Hanoi, Vietnam（越南河内胃肠病与肝病研究所）；Ho Chi Minh University Medical Center（胡志明市大学医疗中心）
本文发布涵盖三国三中心、五种成像模态的 3934 张息肉图像数据集 PolypDB，并提供分割、检测与联邦学习基准，以支持结肠镜 AI 算法开发。
Computer Vision Center（计算机视觉中心）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）；Montreal Institute for Learning Algorithms（蒙特利尔学习算法研究所）；Université de Montréal（蒙特利尔大学）；École Polytechnique de Montréal（蒙特利尔理工学院）；Hospital Clinic（临床医院）；Universidad de Barcelona（巴塞罗那大学）；Imagia Inc.（Imagia公司）
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extend…
Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Nanjing University of Science and Technology（南京理工大学）
The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets are susceptible to unknown anomalies in complex clinical environments. In particular, the high false positive rate of polyp detection remains a major challenge in clinical practice. In this work, we release the FPPD-13 dataset, which provides a taxonomy and real-world cases of typical false positives during computer-aided polyp detection in real-world colonoscopy. We further propose a post-hoc module EndoBoost, whic…
SimulaMet（西穆拉梅特研究所）；UIT The Arctic University of Norway（挪威北极大学）；University of Oxford（牛津大学）；Oslo Metropolitan University（奥斯陆城市大学）
Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions)…
RRTS Dataset: A Benchmark Colonoscopy Dataset from Resource-Limited Settings for Computer-Aided Diagnosis Research
Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh（生物医学工程系，孟加拉国工程大学）；Dhaka Medical College, Dhaka, Bangladesh（达卡医学学院）
REAL-Colon: A dataset for developing real-world AI applications in colonoscopy
Cosmo Intelligent Medical Devices（科斯莫智能医疗设备公司）；Ospedale dei Castelli (N.O.C.)（卡斯特利医院（N.O.C.））；University Hospital of St. Pölten（圣珀尔滕大学医院）；Humanitas University（乌纳塔斯大学）；Humanitas Clinical and Research Center IRCCS（乌纳塔斯临床与研究中心 IRCCS）；Sano Hospital（佐野医院）；Milan Center for Neuroscience, University of Milano–Bicocca（米兰-比可卡大学米兰神经科学中心）
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；Singapore Management University（新加坡管理大学）
Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps. Consequently, they often have high detection errors, especially on challenging polyp cases (e.g., small, flat, or partially visible polyps). In this work, we formulate polyp detection as a weakly-supervised anomaly detection task that uses video-level labelled training data to detect frame-level polyps. In particular, we propose a novel convolutional transforme…
Hanoi University of Science and Technology（河内科技大学）；Hanoi Medical University（河内医科大学）；The Institute of Gastroenterology and Hepatology（胃肠病学与肝病学研究所）；Vietnam National University of Agriculture（越南国立农业大学）
Colorectal cancer is among the most common malignancies and can develop from high-risk colon polyps. Colonoscopy is an effective screening tool to detect and remove polyps, especially in the case of precancerous lesions. However, the missing rate in clinical practice is relatively high due to many factors. The procedure could benefit greatly from using AI models for automatic polyp segmentation, which provide valuable insights for improving colon polyp detection. However, precise segmentation is still challenging due to variations of polyps in size, shape, texture, and color. This paper propos…
Indira Gandhi National Open University（英迪拉·甘地国立开放大学）；Indian Statistical Institute（印度统计研究所）；Northwestern University（西北大学）
The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide. However, the miss rate of colorectal polyp varies significantly among the endoscopists. It is well known that a computer-aided diagnosis (CAD) system can assist endoscopists in detecting colon polyps and minimize the variation among endoscopists. In this study, we introduce a novel deep learning architecture, named MKDCNet, for automatic polyp segmentation robust to significant changes in polyp data distribution. MKDCNet is simply an encoder-decoder n…
University of Saskatchewan（萨斯喀彻温大学）；University of Stanford（斯坦福大学）
Colorectal cancer is the third most common cancer-related death after lung cancer and breast cancer worldwide. The risk of developing colorectal cancer could be reduced by early diagnosis of polyps during a colonoscopy. Computer-aided diagnosis systems have the potential to be applied for polyp screening and reduce the number of missing polyps. In this paper, we compare the performance of different deep learning architectures as feature extractors, i.e. ResNet, DenseNet, InceptionV3, InceptionResNetV2 and SE-ResNeXt in the encoder part of a U-Net architecture. We validated the performance of p…
Indian Statistical Institute（印度统计研究所）；Østfold University College（东福尔德大学学院）；SimulaMet；UiT The Arctic University of Norway（挪威北极大学）；University of Oxford（牛津大学）
Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop and require re-training for each specific dat…
We present a neural network architecture for medical image segmentation of diabetic foot ulcers and colonoscopy polyps. Diabetic foot ulcers are caused by neuropathic and vascular complications of diabetes mellitus. In order to provide a proper diagnosis and treatment, wound care professionals need to extract accurate morphological features from the foot wounds. Using computer-aided systems is a promising approach to extract related morphological features and segment the lesions. We propose a convolution neural network called HarDNet-DFUS by enhancing the backbone and replacing the decoder of …
University of Adelaide（阿德莱德大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；Nagoya University（名古屋大学）
The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps -- such training set is con…
Australian National University（澳大利亚国立大学）；Minjiang University（闽江学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；ETH Zürich（苏黎世联邦理工学院）；UCLA（加州大学洛杉矶分校）；NPU（西北工业大学）
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of large-scale fine-grained segmentation annotations. To address this issue, we first introduce a high-quality frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158,690 colonoscopy frames from the well-known SUN-database. We provide additional annotations with diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, dubbed PN…
Colorectal cancer is a one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper we proposed a polyp segmentation method based on convolutional neural network. Performance of the method is enhanced by two strategies. First, we perform a novel image patch selection method in the training phase of the network. Second, in …
Indira Gandhi National Open University（英迪拉·甘地国立开放大学）；Larkin Community Hospital Palm Springs Campus（拉金社区医院棕榈泉院区）；Long Island Jewish Valley Stream（长岛犹太谷溪医院）；Feinberg School of Medicine, Northwestern University（西北大学范伯格医学院）
Colorectal cancer (CRC) is one of the most common causes of cancer and cancer-related mortality worldwide. Performing colon cancer screening in a timely fashion is the key to early detection. Colonoscopy is the primary modality used to diagnose colon cancer. However, the miss rate of polyps, adenomas and advanced adenomas remains significantly high. Early detection of polyps at the precancerous stage can help reduce the mortality rate and the economic burden associated with colorectal cancer. Deep learning-based computer-aided diagnosis (CADx) system may help gastroenterologists to identify po…
Computerized detection of colonic polyps remains an unsolved issue because of the wide variation in the appearance, texture, color, size, and presence of the multiple polyp-like imitators during colonoscopy. In this paper, we propose a deep convolutional neural network based model for the computerized detection of polyps within colonoscopy images. The proposed model comprises 16 convolutional layers with 2 fully connected layers, and a Softmax layer, where we implement a unique approach using different convolutional kernels within the same hidden layer for deeper feature extraction. We applied…
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
School of Basic Medical Sciences, Fudan University（复旦大学基础医学院）；Shanghai Key Laboratory of MICCAI（上海市MICCAI重点实验室）；Data Science Institute, Imperial College London（伦敦帝国理工学院数据科学研究所）；Shanghai Collaborative Innovation Centre of Endoscopy（上海市内镜微创技术协同创新中心）；Endoscopy Centre and Endoscopy Research Institute, Zhongshan Hospital, Fudan University（复旦大学附属中山医院内镜中心及内镜研究所）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）
University of Kansas（堪萨斯大学）；Ryerson University（瑞尔森大学）
Colonoscopy is a procedure to detect colorectal polyps which are the primary cause for developing colorectal cancer. However, polyp segmentation is a challenging task due to the diverse shape, size, color, and texture of polyps, shuttle difference between polyp and its background, as well as low contrast of the colonoscopic images. To address these challenges, we propose a feature enhancement network for accurate polyp segmentation in colonoscopy images. Specifically, the proposed network enhances the semantic information using the novel Semantic Feature Enhance Module (SFEM). Furthermore, ins…
Department Of Computer Science University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机科学系）
Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. For colorectal cancer, the best screening test available is the colonoscopy. During a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a video of the internal mucosa of the colon. The video data are displayed on a monitor for the physician to examine the lining of the entire colon and check for colorectal polyps. Detection and removal of colorectal polyps are associated with a reduction in mortality from colorectal canc…
University of Oxford（牛津大学）；Oxford National Institute for Health Research Biomedical Research Centre（牛津国家健康研究所生物医学研究中心）；Arab Academy for Science and Technology（阿拉伯科学技术学院）；SimulaMet；UiT The Arctic University of Norway（挪威北极大学）；Middle East Technical University（中东技术大学）；City University of Hong Kong（香港城市大学）；Universitat Pompeu Fabra（庞培法布拉大学）
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy ) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realisin…
FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention
Chongqing University of Posts and Telecommunications（重庆邮电大学）；University of South Dakota（南达科他大学）；Malaviya National Institute of Technology（马拉维亚国立理工学院）；Harvard Medical School（哈佛医学院）；University of Gothenburg（戈尔堡大学）
A multi-centre polyp detection and segmentation dataset for generalisability assessment
University of Leeds（利兹大学）；University of Oxford（牛津大学）；Oxford National Institute for Health Research Biomedical Research Centre（牛津国家健康研究所生物医学研究中心）；SimulaMet（Simula大都会研究中心）；UiT The Arctic University of Norway（挪威北极大学）；Northwestern University（西北大学）；Arab Academy for Science and Technology（阿拉伯科学技术学院）；Veneto Institute of Oncology IOV-IRCCS（威尼托肿瘤研究所IOV-IRCCS）；CRO Centro Riferimento Oncologico IRCCS Aviano（阿维亚诺CRO肿瘤参考中心IRCCS）；University of Alexandria（亚历山大大学）；Université de Versailles St-Quentin en Yvelines（凡尔赛圣康丁昂伊夫利纳大学）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）
University of Cambridge（剑桥大学）；Cancer Research UK Cambridge Centre（英国癌症研究中心剑桥中心）
Background: Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems to support clinicians and reduce the number of polyps missed. Method: In this work we introduce the Focus U-Net, a novel dual attention-gated deep neural network, which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features. The Focus U-Net further incorp…
Norwegian University of Science and Technology (NTNU)（挪威科技大学（NTNU））；Oslo University Hospital（奥斯陆大学医院）；University of Oslo (UiO)（奥斯陆大学（UiO））
One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic polyp image generation is a difficult task in a generative adversarial networks approach. We propose an edge filtering-based combined input conditioned image to train our proposed networks. This enable…
AI-Assisted Colonoscopy: Polyp Detection and Segmentation using Foundation Models
TECNALIA, Basque Research and Technology Alliance (BRTA)（泰克纳利亚，巴斯克研究与技术联盟（BRTA））；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Artificial Intelligence in Science Institute (AISI) at University of California Irvine（加州大学欧文分校科学人工智能研究所）；Instituto de Investigación Sanitaria Gregorio Marañón（格雷戈里奥·马拉尼翁健康研究所）；Jesús Usón Minimally Invasive Surgery Centre（赫苏斯·乌松微创手术中心）；University of the Basque Country（巴斯克大学）
SimulaMet；UiT The Arctic University of Norway（挪威北极大学）；Oslo Metropolitan University（奥斯陆城市大学）；Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Bærum Hospital（贝鲁姆医院）；Augere Medical AS（Augere Medical 公司）；Sahlgrenska University Hospital-Mölndal Hospital（萨尔格伦斯卡大学医院-默恩达尔医院）；Sahlgrenska Academy, University of Gothenburg（哥德堡大学萨尔格伦斯卡学院）
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to de…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Stony Brook University（石溪大学）
Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing polyps (precursors of colon cancer). The non-invasive VC is normally used to inspect a 3D reconstructed colon (from CT scans) for polyps and if found, the OC procedure is performed to physically traverse the colon via endoscope and remove these polyps. In this paper, we present a deep learning framework, Extended and Directional CycleGAN, for lossy unpaired image-to-image translation between OC and VC to augment OC video sequences with scale…
Inception Institute of Artificial Intelligence（人工智能起源研究所）；Wuhan University（武汉大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great importance since it provides valuable information for diagnosis and surgery. However, accurate polyp segmentation is a challenging task, for two major reasons: (i) the same type of polyps has a diversity of size, color and texture; and (ii) the boundary between a polyp and its surrounding mucosa is not sharp. To address these challenges, we propose a parallel reverse attention network (PraNet) for accur…
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
Northwestern University（西北大学）；Jadavpur University（贾达普大学）；Technische Universität Hamburg（汉堡工业大学）；SimulaMet（西穆拉梅特研究所）；Julius-Maximilian University of Würzburg（维尔茨堡大学）；Australian National University（澳大利亚国立大学）；Gachon University（嘉泉大学）；University of Oxford（牛津大学）；University of Bergen（卑尔根大学）；University of Oslo（奥斯陆大学）；Swedish Medical Center（瑞典医疗中心）；National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；University of Science, VNU-HCM（胡志明市国家大学科学大学）
The performance of imaging techniques has an important influence on the clinical diagnostic strategy of colorectal cancer. Linked color imaging (LCI) by laser endoscopy is a recently developed techniques, and its advantage in improving the analysis accuracy of colorectal polyps over white-light (WL) endoscopy has been demonstrated in previous clinical studies. However, there are no objective criteria to evaluate and compare the aforementioned endoscopy methods. This paper presents a new criterion, namely entropy of color gradients image (ECGI), which is based on color gradients distribution an…
Colorectal cancer from the appearance of polyps that can be benign or malignant is one of the most fatal diseases in the world. To find these polyps in patients, colonoscopy is performed, which is a very efficient technique in this case. Clinically, detecting and segmenting these polyps in order to determine their presence or not is a very difficult process that requires a lot of time and experience from professionals, depending directly on these factors. Therefore, it becomes increasingly important to have an automatic, effective and reliable method of detecting and segmenting these polyps, m…
Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a leading cause of cancer deaths in the United States. Colorectal polyps that grow on the intima of the colon or rectum is an important precursor for CRC. Currently, the most common way for colorectal polyp detection and precancerous pathology is the colonoscopy. Therefore, accurate colorectal polyp segmentation during the colonoscopy procedure has great clinical significance in CRC early detection and prevention. In this paper, we propose a novel end-to-end deep learning framework for the colorectal polyp segmentation. …
Xi’an Jiaotong-Liverpool University（西交利物浦大学）；University of Liverpool（利物浦大学）
Colonoscopy, currently the most efficient and recognized colon polyp detection technology, is necessary for early screening and prevention of colorectal cancer. However, due to the varying size and complex morphological features of colonic polyps as well as the indistinct boundary between polyps and mucosa, accurate segmentation of polyps is still challenging. Deep learning has become popular for accurate polyp segmentation tasks with excellent results. However, due to the structure of polyps image and the varying shapes of polyps, it easy for existing deep learning models to overfitting the c…
Colorectal cancer is one of the common cancers in the United States. Polyp is one of the main causes of the colonic cancer and early detection of polyps will increase chance of cancer treatments. In this paper, we propose a novel classification of informative frames based on a convolutional neural network with binarized weights. The proposed CNN is trained with colonoscopy frames along with the labels of the frames as input data. We also used binarized weights and kernels to reduce the size of CNN and make it suitable for implementation in medical hardware. We evaluate our proposed method usin…
Stony Brook University（石溪大学）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for registering pre-treatment virtual (CT) and optical colonoscopies, to guide navigation towards the anomalies found in pre-treatment scans. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays. A new transitive …
Stony Brook University（石溪大学）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the traini…
Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Excision of polyps during colonoscopy helps reduce mortality and morbidity for CRC. Powered by deep learning, computer-aided diagnosis (CAD) systems can detect regions in the colon overlooked by physicians during colonoscopy. Lacking high accuracy and real-time speed are the essential obstacles to be overcome for successful clinical integration of such systems. While literature is focused on improving accuracy, the speed parameter is often ignored. Toward this critical need, we intend to develop a novel real…
Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
The Netherlands Cancer Institute（荷兰癌症研究所）；GROW School for Oncology and Developmental Biology, Maastricht University Medical Center（马斯特里赫特大学医学中心GROW肿瘤与发育生物学学院）
