Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos

There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times

Explainable Information Retrieval using Deep Learning for Medical images

Image segmentation is useful to extract valuable information for an efficient analysis on the region of interest. Mostly, the number of images generated from a real life situation such as streaming video, is large and not ideal for traditional segmentation with machine learning algorithms. This is due to the following factors (a) numerous image features (b) complex distribution of shapes, colors and textures (c) imbalance data ratio of underlying classes (d) movements of the camera, objects and (e) variations in luminance for site capture. So, we have proposed an efficient deep learning model for image classification and the proof-of-concept has been the case studied on gastrointestinal images for bleeding detection. The Explainable Artificial Intelligence (XAI) module has been utilised to reverse engineer the test results for the impact of features on a given test dataset. The architecture is generally applicable in other areas of image classification. The proposed method has been compared with state-of-the-art including Logistic Regression, Support Vector Machine, Artificial Neural Network and Random Forest. It has reported F1 score of 0.76 on the real world streaming dataset which is comparatively better than traditional methods

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

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease

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

Segmentação de imagens da Cápsula Endoscópica com aplicações na deteção de tumores e na reconstrução 3-D da mucosa intestinal

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)A cápsula endoscópica é um dispositivo que contém uma pequena câmara e que é usado para capturar imagens do trato gastrointestinal de forma não invasiva. Como o resultado é um vídeo bastante longo, a taxa de deteção de patologias é baixa, logo existe a necessidade de melhorar a qualidade dos vídeos ao adicionar informação às frames ou então ao fazer uma deteção automática de patologias ou estruturas de relevo. Os objetivos do trabalho descrito nesta dissertação eram especialmente dois. O primeiro consistia na reconstrução tridimensional de frames individuais da cápsula endoscópica, tal como encontrar uma forma de relacionar imagens consecutivas para assim poder, no futuro, juntar informações da terceira dimensão ao longo do vídeo. O segundo objetivo é a deteção automática de tecido tumoral em cada frame retirada da cápsula endoscópica. Para ambos os objetivos foi usado o algoritmo de segmentação Expectation-Maximization, seguindo uma abordagem Maximum a Posteriori, usando o espaço de cor Lab para retirar o efeito da luminosidade. No primeiro caso, é realizada a segmentação com diferentes números de regiões, para aferir o número ótimo em cada frame. Ao calcular os coeficientes de correlação entre vetores de features de cor e textura das várias regiões, é possível detetar regiões correspondentes entre as várias frames. No final a reconstrução das frames é realizada com o uso do método Shape from Shading. Na deteção automática de tumores, depois de dividir a imagem em duas regiões diferentes, o método proposto envolve o cálculo de descritores estatísticos dos histogramas em cada região, e a posterior classificação usando diferentes classificadores supervisionados. Chegou-se à conclusão que a segmentação para encontrar os diferentes tecidos, apesar de apresentar resultados quando analisada individualmente, tem de ser refinada para que as regiões em frames diferentes tenham uma maior semelhança. A reconstrução individual apresenta bons resultados, aumentando a informação em cada frame, aumentando assim a capacidade de diagnóstico. O método de deteção automática de tumores proposto apresenta resultados bastante promissores quando comparados com estado da arte, tanto o método de segmentação, tal como o conjunto de features usado. A precisão do sistema concebido teve uma melhoria de pelo menos 6% em relação a métodos do estado da arte implementados para a mesma base de dados.Wireless Capsule Endoscopy is a non-invasive device that contains a small camera used for capturing images from the gastrointestinal tract. The result of this exam is very long video, which leads to a low detection rate of several pathologies. This fact is the reason why it’s needed for the increase of quality of the videos, either adding new information to every frame, either implementing algorithms for automatic detection of pathologies or important structures. The objectives of the work described in this texto were specially two. The first one consisted in the construction of an algorithm for the 3-D reconstruction of individual frames and also to find similar regions in consecutive regions, so in the future this information could lead to a video of combined 3-D images. The second objective is the automatic detection of tumor tissue in capsule endoscopy frames. The segmentation in both objectives used the Expectation-Maximization algorithm, with a Maximum a Posteriori approach, using the Lab color space so the light effect could be removed. In the first case, the segmentation is performed with a different number of regions, so it could be possible to find the best number of regions in each frame. Correlation coefficients were then calculated between vectors containing color and texture features of the different regions, so corresponding regions could be found. Reconstruction of frames was performed using the Shape from Shading method. In the automatic detection of tumors, after dividing the image in two different regions, the proposed method includes the computation of statistical features from the histograms of each region, and the classification with supervised classifiers. As conclusion, it is possible to say that the segmentation to find different kinds of tissues need to be refined so the correct corresponding regions can be found. The individual reconstruction presents good results, increasing the visual information in each frame, increasing in that way the diagnosis capability. The method for tumor detection presents promising results when compared to state of the art methods. The final system precision had an increase of at least 6% in relation with state of the art algorithm implemented with the same database