Polyp Segmentation with Fully Convolutional Deep Neural Networks—Extended Evaluation Study

Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications—lesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the method’s design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge

GIANA Polyp Segmentation with Fully Convolutional Dilation Neural Networks

Polyp detection and segmentation in colonoscopy images plays an important role in early detection of colorectal cancer. The paper describes methodology adopted for the EndoVisSub2017/2018 Gastrointestinal Image ANAlysis – (GIANA) polyp segmentation sub-challenges. The developed segmentation algorithms are based on the fully convolutional neural network (FCNN) model. Two novel variants of the FCNN have been investigated, implemented and evaluated. The first one, combines the deep residual network and the dilation kernel layers within the fully convolutional network framework. The second proposed architecture is based on the U-net network augmented by the dilation kernels and “squeeze and extraction” units. The proposed architectures have been evaluated against the well-known FCN8 model. The paper describes the adopted evaluation metrics and presents the results on the GIANA dataset. The proposed methods produced competitive results, securing the first place for the SD and HD image segm entation tasks at the 2017 GIANA challenge and the second place for the SD images at the 2018 GIANA challenge

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures

Polyp Segmentation in Colonoscopy Images with Convolutional Neural Networks

The thesis looks at approaches to segmentation of polyps in colonoscopy images. The aim was to investigate and develop methods that are robust, accurate and computationally efficient and which can compete with the current state-of-the-art in polyp segmentation. Colorectal cancer is one of the leading cause of cancer deaths worldwide. To decrease mortality, an assessment of polyp malignancy is performed during colonoscopy examination so polyps can be removed at an early stage. In current routine clinical practice, polyps are detected and delineated manually in colonoscopy images by highly trained clinicians. To automate these processes, machine learning and computer vision techniques have been utilised. They have been shown to improve polyp detectability and segmentation objectivity. However, polyp segmentation is a very challenging task due to inherent variability of polyp morphology and colonoscopy image appearance. This research considers a range of approaches to polyp segmentation – seeking out those that offer a best compromise between accuracy and computational complexity. Based on analysis of existing machine learning and polyp image segmentation techniques, a novel hybrid deep learning segmentation method is proposed to alleviate the impact of the above stated challenges on polyp segmentation. The method consists of two fully convolutional networks. The first proposed network is based on a compact architecture with large receptive fields and multiple classification paths. The method performs well on most images, accurately segmenting polyps of diverse morphology and appearance. However, this network is prone to misdetection of very small polyps. To solve this problem, a second network is proposed, which primarily aims to improve sensitivity to small polyp details by emphasising low-level image features. In order to fully utilise information contained in the available training dataset, comprehensive data augmentation techniques are adopted. To further improve the performance of the proposed segmentation methods, test-time data augmentation is also implemented. A comprehensive multi-criterion analysis of the proposed methods is provided. The result demonstrates that the new methodology has better accuracy and robustness than the current state-of-the-art, as proven by the outstanding performance at the 2017 and 2018 GIANA polyp segmentation challenges

Deep Ensembles Based on Stochastic Activations for Semantic Segmentation

Semantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder structure that aims to capture the semantics of the image and its low-level features. The encoder uses convolutional layers, in general with a stride larger than one, to extract the features, while the decoder recreates the image by upsampling and using skip connections with the first layers. The objective of this study is to propose a method for creating an ensemble of CNNs by enhancing diversity among networks with different activation functions. In this work, we use DeepLabV3+ as an architecture to test the effectiveness of creating an ensemble of networks by randomly changing the activation functions inside the network multiple times. We also use different backbone networks in our DeepLabV3+ to validate our findings. A comprehensive evaluation of the proposed approach is conducted across two different image segmentation problems: the first is from the medical field, i.e., polyp segmentation for early detection of colorectal cancer, and the second is skin detection for several different applications, including face detection, hand gesture recognition, and many others. As to the first problem, we manage to reach a Dice coefficient of 0.888, and a mean intersection over union (mIoU) of 0.825, in the competitive Kvasir-SEG dataset. The high performance of the proposed ensemble is confirmed in skin detection, where the proposed approach is ranked first concerning other state-of-the-art approaches (including HarDNet) in a large set of testing datasets

A comparative study on polyp classification using convolutional neural networks

This work is licensed under a Creative Commons Attribution 4.0 International License.Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called ‘polyp’. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.University of Kansas grant (2228901

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