Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence intervals ranging between (77.08, 77.56) and (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence intervals ranging from (72.20, 73.00) to (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.
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…
Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they need to be precise, interpretable, and uncertainty in predictions must be well understood. In this paper, we develop and evaluate recent advances in uncertainty estimation and model interpretability in the context of semantic segmentation of polyps from colonoscopy images. We evalu…
A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n
University of Bologna（博洛尼亚大学）；Deakin University（迪肯大学）；Tarbiat Modares University（塔比阿特莫达雷斯大学）；University of Birjand（比尔詹德大学）；Technical and Vocational University（技术职业大学）
Purpose: Colorectal cancer (CRC) is the second most common cause of cancer mortality worldwide. Colonoscopy is a widely used technique for colon screening and polyp lesions diagnosis. Nevertheless, manual screening using colonoscopy suffers from a substantial miss rate of polyps and is an overwhelming burden for endoscopists. Computer-aided diagnosis (CAD) for polyp detection has the potential to reduce human error and human burden. However, current polyp detection methods based on object detection framework need many handcrafted pre-processing and post-processing operations or user guidance t…
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 (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. …
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-…
Polyp-D2ATL: Deep Domain-Adaptive Transfer Learning for Colorectal Polyp Classification under Label Distribution Shift
School of Electrical Engineering, Sharif University of Technology（谢尔万大学电气工程学院）；School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院）
Dartmouth College（达特茅斯学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）；Minnesota Gastroenterology PA（明尼苏达胃肠病学协会）；Fairview Southdale Hospital（费尔维尤索斯代尔医院）；University of North Carolina（北卡罗来纳大学）
Histological classification of colorectal polyps plays a critical role in both screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathology slides could benefit clinicians and patients. Evaluate the performance and assess the generalizability of a deep neural network for colorectal polyp classification on histopathology slide images using a multi-institutional dataset. In this study, we developed a deep neural network for classification of four major colorectal polyp types, tubular ade…
Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histopathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using prop…
Bournemouth University（伯恩茅斯大学）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；BCN Medtech（巴塞罗那医疗科技公司）；Universitat Pompeu Fabra（庞培法布拉大学）；ICREA（加泰罗尼亚高等研究院）
Polyps represent an early sign of the development of Colorectal Cancer. The standard procedure for their detection consists of colonoscopic examination of the gastrointestinal tract. However, the wide range of polyp shapes and visual appearances, as well as the reduced quality of this image modality, turn their automatic identification and segmentation with computational tools into a challenging computer vision task. In this work, we present a new strategy for the delineation of gastrointestinal polyps from endoscopic images based on a direct extension of common encoder-decoder networks for se…
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…
Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. Annotated datasets are tedious and time-consuming to produce, especially for physicians who must dedicate their time to their patients. We tackle this issue by proposing a novel framework that can be trained using only weakly annotated images along with exploiting unlabeled images. To this end, we propose three ideas to address this problem, more specifically our contributions are: 1) a novel sparse foreground loss th…
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…
Robert Gordon University（罗伯特戈登大学）；Griffith University（格里菲斯大学）
Colorectal cancer (CRC) is the first cause of death in many countries. CRC originates from a small clump of cells on the lining of the colon called polyps, which over time might grow and become malignant. Early detection and removal of polyps are therefore necessary for the prevention of colon cancer. In this paper, we introduce an ensemble of medical polyp segmentation algorithms. Based on an observation that different segmentation algorithms will perform well on different subsets of examples because of the nature and size of training sets they have been exposed to and because of method-intri…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；MD Anderson Cancer Center（MD安德森癌症中心）
In this study, to address the current high earlydetection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps. Instead of using the common colonoscopic images, we applied three different ML algorithms on the 3D textural image outputs of a unique vision-based surface tactile sensor (VS-TS). To collect realistic textural images of CRC polyps for training the utilized ML classifiers and evaluating their performance, we first designed and additively m…
ERCPMP: An Endoscopic Image and Video Dataset for Colorectal Polyps Morphology and Pathology
Gastroenterology and Liver Disease Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学胃肠病学与肝病研究所胃肠病学与肝病研究中心）；Alborz University of Medical Sciences（厄尔布尔士医科大学）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；Iran University of Medical Sciences（伊朗医科大学）
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…
S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation
The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Hubei University of Technology（湖北工业大学）；University College London（伦敦大学学院）
Colonoscopy is widely recognised as the gold standard procedure for the early detection of colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications, namely lesion detection and classification, providing means to improve accuracy and robustness. The manual segmentation of polyps in colonoscopy images is time-consuming. As a result, the use of deep learning (DL) for automation of polyp segmentation has become important. However, DL-based solutions can be vulnerable to overfitting and the resulting inability to generalise to images captured by different colonosc…
University of Central Florida（中佛罗里达大学）；Mayo Clinic（妙佑医疗国际（梅奥诊所））；University of Catania（卡塔尼亚大学）
Colorectal cancer, largely arising from precursor lesions called polyps, remains one of the leading causes of cancer-related death worldwide. Current clinical standards require the resection and histopathological analysis of polyps due to test accuracy and sensitivity of optical biopsy methods falling substantially below recommended levels. In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps. Our proposed method introduces several technical novelties including a novel capsule architecture with a capsule-average …
We propose a new convolution neural network called HarDNet-MSEG for polyp segmentation. It achieves SOTA in both accuracy and inference speed on five popular datasets. For Kvasir-SEG, HarDNet-MSEG delivers 0.904 mean Dice running at 86.7 FPS on a GeForce RTX 2080 Ti GPU. It consists of a backbone and a decoder. The backbone is a low memory traffic CNN called HarDNet68, which has been successfully applied to various CV tasks including image classification, object detection, multi-object tracking and semantic segmentation, etc. The decoder part is inspired by the Cascaded Partial Decoder, known …
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…
Dartmouth College（达特茅斯学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）
We present an image translation approach to generate augmented data for mitigating data imbalances in a dataset of histopathology images of colorectal polyps, adenomatous tumors that can lead to colorectal cancer if left untreated. By applying cycle-consistent generative adversarial networks (CycleGANs) to a source domain of normal colonic mucosa images, we generate synthetic colorectal polyp images that belong to diagnostically less common polyp classes. Generated images maintain the general structure of their source image but exhibit adenomatous features that can be enhanced with our propose…
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…
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…
SimulaMet（西穆拉大都会研究所）；UiT The Arctic University of Norway（挪威UiT北极大学）；Oslo Metropolitan University（奥斯陆城市大学）；Augere Medical AS（奥杰雷医疗公司）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）；Bærum Hospital（贝鲁姆医院）
Colorectal cancer is the third most common cause of cancer worldwide. According to Global cancer statistics 2018, the incidence of colorectal cancer is increasing in both developing and developed countries. Early detection of colon anomalies such as polyps is important for cancer prevention, and automatic polyp segmentation can play a crucial role for this. Regardless of the recent advancement in early detection and treatment options, the estimated polyp miss rate is still around 20\%. Support via an automated computer-aided diagnosis system could be one of the potential solutions for the over…
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…
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
University of Concordia（康考迪亚大学）；University of Montreal（蒙特利尔大学）；Sunnybrook Health Science Centre（桑尼布鲁克健康科学中心）；Kingston General Hospital（金斯顿总医院）；Mila–Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
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…
Large Language Model Evaluated Stand-alone Attention-Assisted Graph Neural Network with Spatial and Structural Information Interaction for Precise Endoscopic Image Segmentation
Department of Mathematics, The Chinese University of Hong Kong（香港中文大学数学系）；Department of Electronic and Computer Engineering, University of Massachusetts, Lowell（马萨诸塞大学低罗分校电子与计算机工程系）；Department of Radiology, Northwestern University（西北大学放射科）
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…
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 …
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…
Hamburg University of Technology（汉堡工业大学）；Universitätsklinikum Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
Colorectal Cancer(CRC) poses a great risk to public health. It is the third most common cause of cancer in the US. Development of colorectal polyps is one of the earliest signs of cancer. Early detection and resection of polyps can greatly increase survival rate to 90%. Manual inspection can cause misdetections because polyps vary in color, shape, size and appearance. To this end, Computer-Aided Diagnosis systems(CADx) has been proposed that detect polyps by processing the colonoscopic videos. The system acts a secondary check to help clinicians reduce misdetections so that polyps may be resec…
University of Electronic Science and Technology of China（电子科技大学）
Colorectal cancer (CRC) is one of the most common fatal cancer in the world. Polypectomy can effectively interrupt the progression of adenoma to adenocarcinoma, thus reducing the risk of CRC development. Colonoscopy is the primary method to find colonic polyps. However, due to the different sizes of polyps and the unclear boundary between polyps and their surrounding mucosa, it is challenging to segment polyps accurately. To address this problem, we design a Boundary Distribution Guided Network (BDG-Net) for accurate polyp segmentation. Specifically, under the supervision of the ideal Boundary…
Sun Yat-sen University（中山大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Pazhou Lab（琶洲实验室）；The Sixth Affiliated Hospital, Sun Yat-sen University（中山大学附属第六医院）
Automatic and accurate polyp segmentation plays an essential role in early colorectal cancer diagnosis. However, it has always been a challenging task due to 1) the diverse shape, size, brightness and other appearance characteristics of polyps, 2) the tiny contrast between concealed polyps and their surrounding regions. To address these problems, we propose a lesion-aware dynamic network (LDNet) for polyp segmentation, which is a traditional u-shape encoder-decoder structure incorporated with a dynamic kernel generation and updating scheme. Specifically, the designed segmentation head is condi…
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)…
Radboud University Medical Center（拉德堡德大学医学中心）；Ospedale Cannizzaro（坎尼扎罗医院）；Leids Universitair Medisch Centrum（莱顿大学医学中心）；University of Bayreuth（拜罗伊特大学）；Linköping University（林雪平大学）
Artificial Intelligence (AI) can potentially support histopathologists in the diagnosis of a broad spectrum of cancer types. In colorectal cancer (CRC), AI can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs, ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in automated assessment of CRC histopathology whole-slide images. First, we present an AI-based method to segment multiple tissue compartments…
