ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colonoscopy images often provides helpful information for early diagnosis and treatment. Although existing deep learning models often achieve high segmentation performance when tested on the same dataset used in model training; still, their performance often degrades when applied to out-of-distribution datasets, leading to low model generalization or overfitting. This challenge is often associated with the quality of the features learnt from the input images. In this work, a novel Context Feature Refinement (CFR) module is proposed to address the challenge of low model generalization and segmentation performance. The CFR module is built to extract contextual information from the incoming feature map by using multiple parallel convolutional layers with progressively increasing kernel sizes. Using multiple parallel convolutions with different kernel sizes helped to extract more efficient multi-scale contextual information and thus enabled the network to effectively identify and segment small and fine details, as well as larger and more complex structures in the input images. Extensive experiments on three public benchmark datasets in CVC-ClinicDB, Kvasir-SEG, and BKAI-NeoPolyp showed that the proposed ConvSegNet model achieved jaccard, dice and F2 scores of 0.8650, 0.9177, and 0.9328 on CVC-ClinicDB, 0.7936, 0.8618, and 0.8855 on Kvasir-SEG, and 0.8045, 0.8747 and 0.8909 on BKAI-NeoPolyp datasets respectively. Also, an improved generalization performance was achieved by the ConvSegNet model, compared to the benchmark polyp segmentation models. Code is available at https://github.com/AOige/ConvSegNet

Segmentation of the colon in magnetic resonance images

Master'sMASTER OF ENGINEERIN

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

Application of artificial vision algorithms to images of microscopy and spectroscopy for the improvement of cancer diagnosis

El diagnóstico final de la mayoría de tipos de cáncer lo realiza un médico experto en anatomía patológica que examina muestras tisulares o celulares sospechosas extraídas del paciente. Actualmente, esta evaluación depende en gran medida de la experiencia del médico y se lleva a cabo de forma cualitativa mediante técnicas de imagen tradicionales como la microscopía óptica. Esta tarea tediosa está sujeta a altos grados de subjetividad y da lugar a niveles de discordancia inadecuados entre diferentes patólogos, especialmente en las primeras etapas de desarrollo del cáncer. La espectroscopía infrarroja por Transformada de Fourier (siglas FTIR en inglés) es una tecnología ampliamente utilizada en la industria que recientemente ha demostrado una capacidad creciente para mejorar el diagnóstico de diferentes tipos de cáncer. Esta técnica aprovecha las propiedades del infrarrojo medio para excitar los modos vibratorios de los enlaces químicos que forman las muestras biológicas. La principal señal generada consiste en un espectro de absorción que informa sobre la composición química de la muestra iluminada. Los microespectrómetros FTIR modernos, compuestos por complejos componentes ópticos y detectores matriciales de alta sensibilidad, permiten capturar en un laboratorio de investigación común imágenes hiperespectrales de alta calidad que aúnan información química y espacial. Las imágenes FTIR son estructuras de datos ricas en información que se pueden analizar individualmente o junto con otras modalidades de imagen para realizar diagnósticos patológicos objetivos. Por lo tanto, esta técnica de imagen emergente alberga un alto potencial para mejorar la detección y la graduación del riesgo del paciente en el cribado y vigilancia de cáncer. Esta tesis estudia e implementa diferentes metodologías y algoritmos de los campos interrelacionados de procesamiento de imagen, visión por ordenador, aprendizaje automático, reconocimiento de patrones, análisis multivariante y quimiometría para el procesamiento y análisis de imágenes hiperespectrales FTIR. Estas imágenes se capturaron con un moderno microscopio FTIR de laboratorio a partir de muestras de tejidos y células afectadas por cáncer colorrectal y de piel, las cuales se prepararon siguiendo protocolos alineados con la práctica clínica actual. Los conceptos más relevantes de la espectroscopía FTIR se investigan profundamente, ya que deben ser comprendidos y tenidos en cuenta para llevar a cabo una correcta interpretación y tratamiento de sus señales especiales. En particular, se revisan y analizan diferentes factores fisicoquímicos que influyen en las mediciones espectroscópicas en el caso particular de muestras biológicas y pueden afectar críticamente su análisis posterior. Todos estos conceptos y estudios preliminares entran en juego en dos aplicaciones principales. La primera aplicación aborda el problema del registro o alineación de imágenes hiperespectrales FTIR con imágenes en color adquiridas con microscopios tradicionales. El objetivo es fusionar la información espacial de distintas muestras de tejido medidas con esas dos modalidades de imagen y centrar la discriminación en las regiones seleccionadas por los patólogos, las cuales se consideran más relevantes para el diagnóstico de cáncer colorrectal. En la segunda aplicación, la espectroscopía FTIR se lleva a sus límites de detección para el estudio de las entidades biomédicas más pequeñas. El objetivo es evaluar las capacidades de las señales FTIR para discriminar de manera fiable diferentes tipos de células de piel que contienen fenotipos malignos. Los estudios desarrollados contribuyen a la mejora de métodos de decisión objetivos que ayuden al patólogo en el diagnóstico final del cáncer. Además, revelan las limitaciones de los protocolos actuales y los problemas intrínsecos de la tecnología FTIR moderna, que deberían abordarse para permitThe final diagnosis of most types of cancers is performed by an expert clinician in anatomical pathology who examines suspicious tissue or cell samples extracted from the patient. Currently, this assessment largely relies on the experience of the clinician and is accomplished in a qualitative manner by means of traditional imaging techniques, such as optical microscopy. This tedious task is subject to high degrees of subjectivity and gives rise to suboptimal levels of discordance between different pathologists, especially in early stages of cancer development. Fourier Transform infrared (FTIR) spectroscopy is a technology widely used in industry that has recently shown an increasing capability to improve the diagnosis of different types of cancer. This technique takes advantage of the ability of mid-infrared light to excite the vibrational modes of the chemical bonds that form the biological samples. The main generated signal consists of an absorption spectrum that informs of the chemical composition of the illuminated specimen. Modern FTIR microspectrometers, composed of complex optical components and high-sensitive array detectors, allow the acquisition of high-quality hyperspectral images with spatially-resolved chemical information in a common research laboratory. FTIR images are information-rich data structures that can be analysed alone or together with other imaging modalities to provide objective pathological diagnoses. Hence, this emerging imaging technique presents a high potential to improve the detection and risk stratification in cancer screening and surveillance. This thesis studies and implements different methodologies and algorithms from the related fields of image processing, computer vision, machine learning, pattern recognition, multivariate analysis and chemometrics for the processing and analysis of FTIR hyperspectral images. Those images were acquired with a modern benchtop FTIR microspectrometer from tissue and cell samples affected by colorectal and skin cancer, which were prepared by following protocols close to the current clinical practise. The most relevant concepts of FTIR spectroscopy are thoroughly investigated, which ought to be understood and considered to perform a correct interpretation and treatment of its special signals. In particular, different physicochemical factors are reviewed and analysed, which influence the spectroscopic measurements for the particular case of biological samples and can critically affect their later analysis. All these knowledge and preliminary studies come into play in two main applications. The first application tackles the problem of registration or alignment of FTIR hyperspectral images with colour images acquired with traditional microscopes. The aim is to fuse the spatial information of distinct tissue samples measured by those two imaging modalities and focus the discrimination on regions selected by the pathologists, which are meant to be the most relevant areas for the diagnosis of colorectal cancer. In the second application, FTIR spectroscopy is pushed to their limits of detection for the study of the smallest biomedical entities. The aim is to assess the capabilities of FTIR signals to reliably discriminate different types of skin cells containing malignant phenotypes. The developed studies contribute to the improvement of objective decision methods to support the pathologist in the final diagnosis of cancer. In addition, they reveal the limitations of current protocols and intrinsic problems of modern FTIR technology, which should be tackled in order to enable its transference to anatomical pathology laboratories in the future.El diagnòstic final de la majoria de tipus de càncer ho realitza un metge expert en anatomia patològica que examina mostres tissulars o cel¿lulars sospitoses extretes del pacient. Actualment, aquesta avaluació depèn en gran part de l'experiència del metge i es porta a terme de forma qualitativa mitjançant tècniques d'imatge tradicionals com la microscòpia òptica. Aquesta tasca tediosa està subjecta a alts graus de subjectivitat i dóna lloc a nivells de discordança inadequats entre diferents patòlegs, especialment en les primeres etapes de desenvolupament del càncer. L'espectroscòpia infraroja per Transformada de Fourier (sigles FTIR en anglès) és una tecnologia àmpliament utilitzada en la indústria que recentment ha demostrat una capacitat creixent per millorar el diagnòstic de diferents tipus de càncer. Aquesta tècnica aprofita les propietats de l'infraroig mitjà per excitar els modes vibratoris dels enllaços químics que formen les mostres biològiques. El principal senyal generat consisteix en un espectre d'absorció que informa sobre la composició química de la mostra il¿luminada. Els microespectrómetres FTIR moderns, compostos per complexos components òptics i detectors matricials d'alta sensibilitat, permeten capturar en un laboratori d'investigació comú imatges hiperespectrals d'alta qualitat que uneixen informació química i espacial. Les imatges FTIR són estructures de dades riques en informació que es poden analitzar individualment o juntament amb altres modalitats d'imatge per a realitzar diagnòstics patològics objectius. Per tant, aquesta tècnica d'imatge emergent té un alt potencial per a millorar la detecció i la graduació del risc del pacient en el cribratge i vigilància de càncer. Aquesta tesi estudia i implementa diferents metodologies i algoritmes dels camps interrelacionats de processament d'imatge, visió per ordinador, aprenentatge automàtic, reconeixement de patrons, anàlisi multivariant i quimiometria per al processament i anàlisi d'imatges hiperespectrals FTIR. Aquestes imatges es van capturar amb un modern microscopi FTIR de laboratori a partir de mostres de teixits i cèl¿lules afectades per càncer colorectal i de pell, les quals es van preparar seguint protocols alineats amb la pràctica clínica actual. Els conceptes més rellevants de l'espectroscòpia FTIR s'investiguen profundament, ja que han de ser compresos i tinguts en compte per dur a terme una correcta interpretació i tractament dels seus senyals especials. En particular, es revisen i analitzen diferents factors fisicoquímics que influeixen en els mesuraments espectroscòpiques en el cas particular de mostres biològiques i poden afectar críticament la seua anàlisi posterior. Tots aquests conceptes i estudis preliminars entren en joc en dues aplicacions principals. La primera aplicació aborda el problema del registre o alineació d'imatges hiperespectrals FTIR amb imatges en color adquirides amb microscopis tradicionals. L'objectiu és fusionar la informació espacial de diferents mostres de teixit mesurades amb aquestes dues modalitats d'imatge i centrar la discriminació en les regions seleccionades pels patòlegs, les quals es consideren més rellevants per al diagnòstic de càncer colorectal. En la segona aplicació, l'espectroscòpia FTIR es porta als seus límits de detecció per a l'estudi de les entitats biomèdiques més xicotetes. L'objectiu és avaluar les capacitats dels senyals FTIR per discriminar de manera fiable diferents tipus de cèl¿lules de pell que contenen fenotips malignes. Els estudis desenvolupats contribueixen a la millora de mètodes de decisió objectius que ajuden el patòleg en el diagnòstic final del càncer. A més, revelen les limitacions dels protocols actuals i els problemes intrínsecs de la tecnologia FTIR moderna, que haurien d'abordar per permetre la seva transferència als laboratoris d'anatomia patològica en el futur.Peñaranda Gómez, FJ. (2018). Application of artificial vision algorithms to images of microscopy and spectroscopy for the improvement of cancer diagnosis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99748TESI

Estimation of gastrointestinal polyp size in video endoscopy

Abstract Worldwide the colorectal cancer is one of the most common public health problems, constituting in 2010 the seventh cause of death. This aggressive cancer is firstly identified during an endoscopy routine examination by characterizing a set of polyps that appear along the digestive tract, mainly in the colon and rectum. The polyp size is one of the most important features that determines the surgical endoscopy management and even can be used to predict the level of aggressiveness. For instance, the gastroenterologists only send a polyp sample to the pathology examination if the polyp diameter is larger than 10 mm, a measure that is achieved typically by examining the lesion with a calibrated endoscopy tool. However, the polyp size measure is very challenging because it must be performed during a procedure subjected to a complex mix of noise sources, such as: the distorted optical characteristics of the endoscopy, the exacerbated physiological conditions and abrupt motion. The main goal of this thesis was estimated the polyp size during an endoscopy video sequence using a spatio-temporal characterization. Firstly, the method estimated the region with more motion within which the polyp shape is approximated by those pixels with the largest temporal variance. On the above, an initial manual polyp delineation in the first frame captures the main features to be follow in posterior frames by a cross correlation procedure. Afterwards, a bayesian tracking strategy is used to refine the polyp segmentation. Finally a defocus strategy allows to estimate on the clear cut frame at a certain depth as a reference to determine the polyp size obtaining reliable results. In the segmentation task, the approach achieved a Dice Score of 0.7 in real endoscopy video-sequences, when comparing with an expert. In addition, the results polyp size estimation obtained a Root Mean Square Error (RMSE) of 0.87 mm with spheres of known size that simulated the polyps, and in real endoscopy sequences obtaining a RMSE of 4.7 mm compared with measures obtained by a group of four experts with similar experience.El cáncer colorectal es uno de los problemas de salud pública más comunes a nivel mundial, ocupando la séptima causa de muerte en el 2010. Este tipo de cáncer tan agresivo es identificado prematuramente por un conjunto de pólipos que crecen a lo largo del tracto digestivo, principalmente en el colon y el recto. El tamaño de los pólipos es una de las características mas importantes, con la cual se determina el manejo quirúrgico de la lesión e incluso puede ser usado para predecir el grado de malignidad. Acorde a esto, el experto solo envía una muestra del pólipo para un examen patológico, sí el diámetro del pólipo es más largo que 10 mm. típica mente, esta medida es tomada examinando la lesión con una herramienta endoscópica calibrada. Sin embargo, la medición del tamaño del pólipo es realmente difícil debido a que el procedimiento está sujeto a fuentes de ruido bastante complejas, tales como: la distorsión óptica que es característica del endoscopio, las condiciones fisiológicas del tracto digestivo y los movimientos abruptos con el dispositivo. La contribución principal de este trabajo fue la estimación del tamaño de los pólipos, sobre una secuencia de vídeo de un procedimiento de endoscopia usando una caracterización espacio-temporal. En primera parte, el método estima la región con mayor movimiento que corresponde aproximadamente a la región del pólipo, tomando aquellos pixeles con mayor varianza temporal. Sobre lo anterior, una delineación manual de la lesión es realizada en el primer cuadro para establecer las principales características, para ser seguidas en los cuadros posteriores usando un método de correlación cruzada. Después, se usó una estrategia de seguimiento bayesiana para refinar la segmentación del pólipo. Finalmente, una estrategia basada en la correspondencia del desenfoque de las imágenes de una secuencia a una profundidad o distancia determinada, se pudo obtener una referencia para determinar el tamaño de los pólipos, obteniendo resultados fiables. En la etapa de segmentación, la estrategia logra un Dice score de 0, 7 al comparar con un experto en secuencias de endoscopia reales. Y en la estimación del tamaño de los pólipo se obtuvo un error cuadrático medio (RMSE) de 0.87 mm, comparando con esferas de tamaño conocido que simulaban los pólipos, y en secuencias de endoscopia reales se obtuvo un RMSE de 4.7 mm comparando con las medidas obtenidas por un grupo de cuatro. expertos con experiencia similar.Maestrí

Endoluminal surface registration for CT colonography using Haustral Fold Matching

Computed Tomographic (CT) colonography is a technique used for the detection of bowel cancer or potentially precancerous polyps. The procedure is performed routinely with the patient both prone and supine to differentiate fixed colonic pathology from mobile faecal residue. Matching corresponding locations is difficult and time consuming for radiologists due to colonic deformations that occur during patient repositioning. We propose a novel method to establish correspondence between the two acquisitions automatically. The problem is first simplified by detecting haustral folds using a graph cut method applied to a curvature-based metric applied to a surface mesh generated from segmentation of the colonic lumen. A virtual camera is used to create a set of images that provide a metric for matching pairs of folds between the prone and supine acquisitions. Image patches are generated at the fold positions using depth map renderings of the endoluminal surface and optimised by performing a virtual camera registration over a restricted set of degrees of freedom. The intensity difference between image pairs, along with additional neighbourhood information to enforce geometric constraints over a 2D parameterisation of the 3D space, are used as unary and pair-wise costs respectively, and included in a Markov Random Field (MRF) model to estimate the maximum a-posteriori fold labelling assignment. The method achieved fold matching accuracy of 96.0% and 96.1% in patient cases with and without local colonic collapse. Moreover, it improved upon an existing surface-based registration algorithm by providing an initialisation. The set of landmark correspondences is used to non-rigidly transform a 2D source image derived from a conformal mapping process on the 3D endoluminal surface mesh. This achieves full surface correspondence between prone and supine views and can be further refined with an intensity based registration showing a statistically significant improvement (p<0.001p<0.001), and decreasing mean error from 11.9mm11.9mm to 6.0mm6.0mm measured at 1743 reference points from 17 CTC datasets

Nonlinear effects in finite elements analysis of colorectal surgical clamping

Minimal Invasive Surgery (MIS) is a procedure that has increased its applications in past few years in different types of surgeries. As number of application fields are increasing day by day, new issues have been arising. In particular, instruments must be inserted through a trocar to access the abdominal cavity without capability of direct manipulation of tissues, so a loss of sensitivity occurs. Generally speaking, the student of medicine or junior surgeons need a lot of practice hours before starting any surgical procedure, since they have to difficulty in acquiring specific skills (hand–eye coordination among others) for this type of surgery. Here is what the surgical simulator present a promising training method using an approach based on Finite Element Method (FEM). The use of continuum mechanics, especially Finite Element Analysis (FEA) has gained an extensive application in medical field in order to simulate soft tissues. In particular, colorectal simulations can be used to understand the interaction between colon and the surrounding tissues and also between colon and instruments. Although several works have been introduced considering small displacements, FEA applied to colorectal surgical procedures with large displacements is a topic that asks for more investigations. This work aims to investigate how FEA can describe non-linear effects induced by material properties and different approximating geometries, focusing as test-case application colorectal surgery. More in detail, it shows a comparison between simulations that are performed using both linear and hyperelastic models. These different mechanical behaviours are applied on different geometrical models (planar, cylindrical, 3D-SS and a real model from digital acquisitions 3D-S) with the aim of evaluating the effects of geometric non-linearity. Final aim of the research is to provide a preliminary contribution to the simulation of the interaction between surgical instrument and colon tissues with multi-purpose FEA in order to help the preliminary set-up of different bioengineering tasks like force-contact evaluation or approximated modelling for virtual reality (surgical simulations). In particular, the contribution of this work is focused on the sensitivity analysis of the nonlinearities by FEA in the tissue-tool interaction through an explicit FEA solver. By doing in this way, we aim to demonstrate that the set-up of FEA computational surgical tools may be simplified in order to provide assistance to non-expert FEA engineers or medicians in more precise way of using FEA tools