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

Fisher encoding of convolutional neural network features for endoscopic image classification

We propose an approach for the automated diagnosis of celiac disease (CD) and colonic polyps (CP) based on applying Fisher encoding to the activations of convolutional layers. In our experiments, three different convolutional neural network (CNN) architectures (AlexNet, VGG-f, and VGG-16) are applied to three endoscopic image databases (one CD database and two CP databases). For each network architecture, we perform experiments using a version of the net that is pretrained on the ImageNet database, as well as a version of the net that is trained on a specific endoscopic image database. The Fisher representations of convolutional layer activations are classified using support vector machines. Additionally, experiments are performed by concatenating the Fisher representations of several layers to combine the information of these layers. We will show that our proposed CNN-Fisher approach clearly outperforms other CNN- and non-CNN-based approaches and that our approach requires no training on the target dataset, which results in substantial time savings compared with other CNN-based approaches.(VLID)295911

Automatic Esophageal Abnormality Detection and Classification

Esophageal cancer is counted as one of the deadliest cancers worldwide ranking the sixth among all types of cancers. Early esophageal cancer typically causes no symp- toms and mainly arises from overlooked/untreated premalignant abnormalities in the esophagus tube. Endoscopy is the main tool used for the detection of abnormalities, and the cell deformation stage is confirmed by taking biopsy samples. The process of detection and classification is considered challenging for several reasons such as; different types of abnormalities (including early cancer stages) can be located ran- domly throughout the esophagus tube, abnormal regions can have various sizes and appearances which makes it difficult to capture, and failure in discriminating between the columnar mucosa from the metaplastic epithelium. Although many studies have been conducted, it remains a challenging task and improving the accuracy of auto- matically classifying and detecting different esophageal abnormalities is an ongoing field. This thesis aims to develop novel automated methods for the detection and classification of the abnormal esophageal regions (precancerous and cancerous) from endoscopic images and videos. In this thesis, firstly, the abnormality stage of the esophageal cell deformation is clas- sified from confocal laser endomicroscopy (CLE) images. The CLE is an endoscopic tool that provides a digital pathology view of the esophagus cells. The classifica- tion is achieved by enhancing the internal features of the CLE image, using a novel enhancement filter that utilizes fractional integration and differentiation. Different imaging features including, Multi-Scale pyramid rotation LBP (MP-RLBP), gray level co-occurrence matrices (GLCM), fractal analysis, fuzzy LBP and maximally stable extremal regions (MSER), are calculated from the enhanced image to assure a robust classification result. The support vector machine (SVM) and random forest (RF) classifiers are employed to classify each image into its pathology stage. Secondly, we propose an automatic detection method to locate abnormality regions from high definition white light (HD-WLE) endoscopic images. We first investigate the performance of different deep learning detection methods on our dataset. Then we propose an approach that combines hand-designed Gabor features with extracted convolutional neural network features that are used by the Faster R-CNN to detect abnormal regions. Moreover, to further improve the detection performance, we pro- pose a novel two-input network named GFD-Faster RCNN. The proposed method generates a Gabor fractal image from the original endoscopic image using Gabor filters. Then features are learned separately from the endoscopic image and the gen- erated Gabor fractal image using the densely connected convolutional network to detect abnormal esophageal regions. Thirdly, we present a novel model to detect the abnormal regions from endoscopic videos. We design a 3D Sequential DenseConvLstm network to extract spatiotem- poral features from the input videos that are utilized by a region proposal network and ROI pooling layer to detect abnormality regions in each frame throughout the video. Additionally, we suggest an FS-CRF post-processing method that incorpor- ates the Conditional Random Field (CRF) on a frame-based level to recover missed abnormal regions in neighborhood frames within the same clip. The methods are evaluated on four datasets: (1) CLE dataset used for the classific- ation model, (2) Publicly available dataset named Kvasir, (3) MICCAI’15 Endovis challenge dataset, Both datasets (2) and (3) are used for the evaluation of detection model from endoscopic images. Finally, (4) Gastrointestinal Atlas dataset used for the evaluation of the video detection model. The experimental results demonstrate promising results of the different models and have outperformed the state-of-the-art methods

World Journal of Gastroenterology / Computer-aided texture analysis combined with experts' knowledge : improving endoscopic celiac disease diagnosis

AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease (CD). METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computer-based classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique (MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts> visually classified each image as normal mucosa (Marsh-0) or villous atrophy (Marsh-3). The experts decisions were further integrated into state-of-the-art texture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts diagnoses in 27 different settings. RESULTS: Compared to the experts diagnoses, in 24 of 27 classification settings (consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant (P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95% (P < 0.001). CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.KLI 429-B13(VLID)215382

Familial adenomatous polyposis : new insights into the craniofacial radiograph features

Tese (doutorado) — Universidade de Brasília, Faculdade de Agronomia e Medicina Veterinária, Programa de Pós-Graduação em Saúde Animal, 2020.A Polipose Adenomatosa Familial (FAP) é uma doença com padrão de herança autossômico dominante predisponente ao câncer colorretal. No Brasil, o câncer colorretal está entre as quatro neoplasias malignas mais frequentes e é o terceiro em mortalidade em ambos os sexos. Os pacientes com FAP, além de apresentarem manifestações intestinais, apresentam alterações dento-ósseas. Dentre essas, são relatados presença de osteomas, odontomas, dentes supranumerários, escleroses ósseas no complexo maxilomandibular que podem se manifestar precocemente - antes do aparecimento dos pólipos intestinais. Uma revisão sistemática da literatura demonstrou a importância da investigação de doenças sistêmicas por meio de alterações ósseas presentes em radiografias panorâmicas - rotineiramente requisitadas por cirurgiões-dentistas. Baseando em artigos incluídos na revisão, regiões de interesse foram mapeadas como pontos de referência para uma futura área de análise de índices radiomorfométricos. Alterações ósseas foram detectadas quando condiçōes sistêmicas acometiam os pacientes. Um segundo projeto demonstrou que o trabeculado ósseo mandibular de pacientes FAP, quando comparados com controles pareados, apresentou alterações micro estruturais no osso trabecular mandibular quando submetidos a análise de dimensão fractal. Numa tentativa de englobar pacientes pediátricos e adultos em países diferentes, um estudo multicêntrico foi elaborado em parceria com a Universidade de Brasília e o Mercy’s Children Hospital nos Estados Unidos. Pacientes pediátricos FAP mostraram alterações ósseas similares aos adultos. Quando esses pacientes foram comparados aos controles, os pacientes FAP apresentaram alterações no padrão trabeculado ósseo, além de alterações dentais. Esse último estudo têm como objetivo recomendar o acompanhamento odontológico periódico através de radiografia panorâmica convencional anual em pacientes FAP e nas famílias em risco. Além de enfatizar a necessidade de participação do dentista nas equipes médicas multiprofissionais que acompanham essas famílias. Assim, esse trabalho alerta e conscientiza de forma crítica, baseada em evidências, nas equipes de saúde bucal sobre a importância de investigar doenças sistêmicas, alterações ósseas e FAP nos exames radiográficos rotineiros.Familial Adenomatous Polyposis (FAP) is an autosomal dominant disorder caused by mutations in the Adenomatous Polyposis Coli gene (APC). Worldwide, colorectal cancer (CRC) is within the third most frequent malignant neoplasm. CRC ranked as the third modality associated-death with females and males. The FAP patients, in addition to present extraintestinal manifestations, also show dento-osseous alterations. These alterations are mostly associated with odontomas, osteomas, supernumerary teeth, and idiopathic osteosclerosis. These last could precede the clinical evidence of intestinal polyps. A systemic review of the literature demonstrated the importance of the systemic disease investigation through mandibular trabecular bone alterations using conventional panoramic radiographs – which are routinely prescribed by dentists in general practice. Based on the articles included in this systematic review, regions of interest were mapped and used as reference-points to investigate radiomorphometric indexes. Besides, trabecular and cortical bone alterations were possibly associated with systemic conditions. A second project demonstrated that the mandibular trabecular bone pattern in FAP patients when compared to healthy individuals, showed texture discrepancies and narrow bone alterations via the fractal dimension analysis. In an attempt to radiographically assess FAP children and adults in different locations, we developed a multicentric study in partnership with the Children’s Mercy Hospital in Kansas City, United States. Pediatric FAP demonstrated osseous alterations that were similar to the adults affected by the same disease. Compared to the healthy controls, the FAP patients, presented alterations in the trabecular bone texture of the mandible. These studies aim to recommend the annual dental follow-up on the FAP patients and families at risk using the panoramic radiograph. In addition to emphasizing the importance of a dentist collaborating in the FAP multispecialty team. Thus, our objective is to alert and create critical thinking, based on scientific evidence, in the dental health teams about the importance of the opportunistic surveillance and screening of systemic diseases and FAP extraintestinal manifestations on the routinely taken dental radiographs

Early diagnosis of cancer using LSS

Thesis (Ph. D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 2001.Includes bibliographical references.This thesis presents a novel optical technique, light scattering spectroscopy (LSS), developed for quantitative characterization of tissue morphology as well as in vivo detection and diagnosis of the diseases associated with alteration of normal tissue structure such as precancerous and early cancerous transformations in various epithelia. LSS employs a wavelength dependent component of light scattered by epithelial cells to obtain information about subcellular structures, such as cell nuclei. Since nuclear atypia is one of the hallmarks of precancerous and cancerous changes in most human tissues, the technique has the potential to provide a broadly applicable means of detecting epithelial precancerous lesions and noninvasive cancers in various organs, which can be optically accessed either directly or by means of optical fibers. We have developed several types of LSS instrumentation including 1) endoscopically compatible LSS-based fiber-optic system;(cont.) 2) LSS-based imaging instrumentation, which allows mapping quantitative parameters characterizing nuclear properties over wide, several cm2, areas of epithelial lining; and 3) scattering angle sensitive LSS instrumentation (a/LSS), which enables to study the internal structure of cells and their organelles, i.e. nuclei, on a submicron scale. Multipatient clinical studies conducted to test the diagnostic potential of LSS in five organs (esophagus, colon, bladder, cervix and oral cavity) have shown the generality and efficacy of the technique and indicated that LSS may become an important tool for early cancer detection as well as better biological understanding of the disease.by Vadim Backman.Ph.D

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition