A fully automatic CAD-CTC system based on curvature analysis for standard and low-dose CT data

Computed tomography colonography (CTC) is a rapidly evolving noninvasive medical investigation that is viewed by radiologists as a potential screening technique for the detection of colorectal polyps. Due to the technical advances in CT system design, the volume of data required to be processed by radiologists has increased significantly, and as a consequence the manual analysis of this information has become an increasingly time consuming process whose results can be affected by inter- and intrauser variability. The aim of this paper is to detail the implementation of a fully integrated CAD-CTC system that is able to robustly identify the clinically significant polyps in the CT data. The CAD-CTC system described in this paper is a multistage implementation whose main system components are: 1) automatic colon segmentation; 2) candidate surface extraction; 3) feature extraction; and 4) classification. Our CAD-CTC system performs at 100% sensitivity for polyps larger than 10 mm, 92% sensitivity for polyps in the range 5 to 10 mm, and 57.14% sensitivity for polyps smaller than 5 mm with an average of 3.38 false positives per dataset. The developed system has been evaluated on synthetic and real patient CT data acquired with standard and low-dose radiation levels

Segmentation and polyp detection in virtual colonoscopy : a complete system for computer aided diagnosis

El cancer colorectal es una de las mayores causas de muerte por cancer en el mundo. La deteccion temprana de polipos es fundamental para su tratamiento, permitiendo alcanzar tasas del 90% de curabilidad. La tecnica habitual para la deteccion de polipos, debido a su elevada performance, es la colonoscopia optica (tecnica invasiva y extremadamente cara). A mediados de los '90 surge la tecnica denominada colonoscopia virtual. Esta tecnica consiste en la reconstruccion 3D del colon a partir de cortes de tomografia computada. Es por ende una tecnica no invasiva, y relativamente barata, pero la cantidad de falsos positivos y falsos negativos producida por estos metodos esta muy por encima de los maximos aceptados en la practica medica. Los avances recientes en las tecnicas de imagenologia parecerian hacer posible la reduccion de estas tasas. Como consecuencia de esto, estamos asistiendo a un nuevo interes por la colonoscopia virtual. En este trabajo se presenta un sistema completo de diagnostico asistido por computadora. La primera etapa del sistema es la segmentacion, que consiste en la reconstruccion 3D de la superficie del colon a partir del volumen tomografico. El aporte principal en este paso es el suavizado de la imagen. A partir de la superficie, se detectan aquellas zonas candidatas de ser polipos mediante una estrategia multi-escala que permite delinear con precision la zona. Luego para cada candidato se extraen caracteristicas geometricas y de textura, que son calculadas tambien en el tejido que rodea la zona a efectos de compararlas. Finalmente las zonas candidatas se clasifican utilizando SVM. Los resultados obtenidos son prometedores, permitiendo detectar un 100% de los polipos mayoresColorectal cancer is the second leading cause of cancer-related death in the United States, and the third cause worldwide. The early detection of polyps is fundamental, allowing to reduce mortality rates up to 90%. Nowadays, optical colonoscopy is the most used detection method due in part to its relative high performance. Virtual Colonoscopy is a promising alternative technique that emerged in the 90's. It uses volumetric Computed Tomographic data of the cleansed and air-distended colon, and the examination is made by a specialist from the images in a computer. Therefore, this technique is less invasive and less expensive than optical colonoscopy, but up to now the false positive and false negative rates are above the accepted medical limits. Recent advances in imaging techniques have the potential to reduce these rates; consequently, we are currently re-experiencing an increasing interest in Virtual Colonoscopy. In this work we propose a complete pipeline for a Computer-Aided Detection algorithm. The system starts with a novel and simple segmentation step. We then introduce geometrical and textural features that take into account not only the candidate polyp region, but the surrounding area at multiple scales as well. This way, our proposed CAD algorithm is able to accurately detect candidate polyps by measuring local variations of these features. Candidate patches are then classi ed using SVM. The whole algorithm is completely automatic and produces state-of-the-art results, achieving 100% sensitivity for polyps greater than 6mm in size with less than one false positive per case, and 100% sensitivity for polyps greater than 3mm in size with 2:2 false positives per case

Chip de Vision Intelligent pour l'exploration de côlon

International audienceIn this article, we present our work on our processing chain to realize a Wireless Capsule Endoscopy (WCE) including a Smart Vision Chip (SVC). We obtain a sensitivity of 74.87% and a specificity of 88.31% on a large database, that we have constructed, composed of 18910 images containing 3895 polyps from 20 different video-colonoscopies.Dans cette article, on présente notre travail sur notre chaine de traitement pour réaliser une Capsule endoscopique Intelligent iWCE en intégrant une Chip de vision intelligente. Nous avons obtenu une sensibilité de 74.87% et une spécificité de 88.31% validé sur une grande base de données compris de 18910 images à partir de 20 films de coloscopie

Vision-based retargeting for endoscopic navigation

Endoscopy is a standard procedure for visualising the human gastrointestinal tract. With the advances in biophotonics, imaging techniques such as narrow band imaging, confocal laser endomicroscopy, and optical coherence tomography can be combined with normal endoscopy for assisting the early diagnosis of diseases, such as cancer. In the past decade, optical biopsy has emerged to be an effective tool for tissue analysis, allowing in vivo and in situ assessment of pathological sites with real-time feature-enhanced microscopic images. However, the non-invasive nature of optical biopsy leads to an intra-examination retargeting problem, which is associated with the difficulty of re-localising a biopsied site consistently throughout the whole examination. In addition to intra-examination retargeting, retargeting of a pathological site is even more challenging across examinations, due to tissue deformation and changing tissue morphologies and appearances. The purpose of this thesis is to address both the intra- and inter-examination retargeting problems associated with optical biopsy. We propose a novel vision-based framework for intra-examination retargeting. The proposed framework is based on combining visual tracking and detection with online learning of the appearance of the biopsied site. Furthermore, a novel cascaded detection approach based on random forests and structured support vector machines is developed to achieve efficient retargeting. To cater for reliable inter-examination retargeting, the solution provided in this thesis is achieved by solving an image retrieval problem, for which an online scene association approach is proposed to summarise an endoscopic video collected in the first examination into distinctive scenes. A hashing-based approach is then used to learn the intrinsic representations of these scenes, such that retargeting can be achieved in subsequent examinations by retrieving the relevant images using the learnt representations. For performance evaluation of the proposed frameworks, extensive phantom, ex vivo and in vivo experiments have been conducted, with results demonstrating the robustness and potential clinical values of the methods proposed.Open Acces

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differences in contribution between different-level features; and 2) designing an effective mechanism for fusing these features. Different from existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations. In addition, considering the image acquisition influence and elusive properties of polyps, we introduce three novel modules, including a cascaded fusion module (CFM), a camouflage identification module (CIM), and a similarity aggregation module (SAM). Among these, the CFM is used to collect the semantic and location information of polyps from high-level features, while the CIM is applied to capture polyp information disguised in low-level features. With the help of the SAM, we extend the pixel features of the polyp area with high-level semantic position information to the entire polyp area, thereby effectively fusing cross-level features. The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities. Extensive experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations (e.g., appearance changes, small objects) than existing methods, and achieves the new state-of-the-art performance. The proposed model is available at https://github.com/DengPingFan/Polyp-PVT.Comment: Technical Repor

Two-layer ensemble of deep learning models for medical image segmentation. [Article]

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification

Automatic colonic polyp detection using curvature analysis for standard and low dose CT data

Colon cancer is the second leading cause of cancer related deaths in the developed nations. Early detection and removal of colorectal polyps via screening is the most effective way to reduce colorectal cancer (CRC) mortality. Computed Tomography Colonography (CTC) or Virtual Colonoscopy (VC) is a rapidly evolving non-invasive technique and the medical community view this medical procedure as an alternative to the standard colonoscopy for the detection of colonic polyps. In CTC the first step for automatic polyp detection for 3D visualization of the colon structure and automatic polyp detection addresses the segmentation of the colon lumen. The segmentation of colon lumen is far from a trivial task as in practice many datasets are collapsed due to incorrect patient preparation or blockages caused by residual water/materials left in the colon. In this thesis a robust multi-stage technique for automatic segmentation of the colon is proposed tha t maximally uses the anatomical model of a generic colon. In this regard, the colon is reconstructed using volume by length analysis, orientation, length, end points, geometrical position in the volumetric data, and gradient of the centreline of each candidate air region detected in the CT data. The proposed method was validated using a total of 151 standard dose (lOOmAs) and 13 low-dose (13mAs-40mAs) datasets and the collapsed colon surface detection was always higher than 95% with an average of 1.58% extra colonic surface inclusion. The second major step of automated CTC attempts the identification of colorectal polyps. In this thesis a robust method for polyp detection based on surface curvature analysis has been developed and evaluated. The convexity of the segmented colon surface is sampled using the surface normal intersection, Hough transform, 3D histogram, Gaussian distribution, convexity constraint and 3D region growing. For each polyp candidate surface the morphological and statistical features are extracted and the candidate surface is classified as a polyp/fold structure using a Feature Normalized Nearest Neighbourhood classifier. The devised polyp detection scheme entails a low computational overhead (typically takes 3.60 minute per dataset) and shows 100% sensitivity for polyps larger than 10mm, 92% sensitivity for polyps in the range 5 to 10mm and 64.28% sensitivity for polyp smaller than 5mm. The developed technique returns in average 4.01 false positives per dataset. The patient exposure to ionising radiation is the major concern in using CTC as a mass screening technique for colonic polyp detection. A reduction of the radiation dose will increase the level of noise during the acquisition process and as a result the quality of the CT d a ta is degraded. To fully investigate the effect of the low-dose radiation on the performance of automated polyp detection, a phantom has been developed and scanned using different radiation doses. The phantom polyps have realistic shapes (sessile, pedunculated, and flat) and sizes (3 to 20mm) and were designed to closely approximate the real polyps encountered in clinical CT data. Automatic polyp detection shows 100% sensitivity for polyps larger than 10mm and shows 95% sensitivity for polyps in the range 5 to 10mm. The developed method was applied to CT data acquired at radiation doses between 13 to 40mAs and the experimental results indicate th a t robust polyp detection can be obtained even at radiation doses as low as 13mAs

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques