Building up the Future of Colonoscopy – A Synergy between Clinicians and Computer Scientists

Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly

A benchmark for endoluminal scene segmentation of colonoscopy images

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization

A Benchmark for endoluminal scene segmentation of colonoscopy images

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization

Endoscopic diagnosis of H. pylori infection

[spa] La infección por Helicobacter pylori (H. pylori) es muy prevalente en nuestro medio, y se asocia a enfermedad gástrica muy relevante, tanto benigna como maligna. El patrón oro para su diagnóstico sigue siendo, a día de hoy, la confirmación histológica por biopsia. Sin embargo, cada vez hay más evidencia de que el diagnóstico endoscópico óptico podría tener un papel fundamental que permitiría ahorrar biopsias innecesarias en casos determinados. Concretamente, la distribución regular de las vénulas colectoras (patrón RAC) parece tener un alto valor predictivo negativo (VPN) para descartar dicha infección. En la presente revisión se describen los hallazgos endoscópicos más destacados y con mejor potencial diagnóstico para la infección por H. pylori tras una búsqueda exhaustiva comparando los estudios más relevantes que se han llevado a cabo en Europa y Oriente.[eng] Helicobacter pylori (H. pylori) infection is highly prevalent in our environment and is associated with highly relevant gastric disease, both benign and malignant. The gold standard for diagnosis is histological confirmation by biopsy. However, there is increasing evidence that optical endoscopic diagnosis could be a fundamental role in avoiding unnecessary biopsies in certain cases. Specifically, the regular distribution of the collecting venules (RAC pattern) seems to have a high negative predictive value (NPV) to rule out infection. This review describes the most outstanding endoscopic findings with the best diagnostic potential for H. pylori infection after an exhaustive search comparing the most relevant studies that have been carried out in Europe and the East

Endoscopic submucosal dissection in Spain: outcomes and development possibilities

Endoscopic submucosal dissection (ESD) allows endoscopic, curative, en-bloc resection of superficial malignant or premalignant lesions. This procedure was conceived over 10 years ago in Japan, but has not experienced great expansion in Western countries for different reasons. This article reviews ESD indications and outcomes, and reflects on the reasons that prevent ESD from becoming common clinical practice in Western hospitals. Finally, recommendations on ESD training in our setting are made

Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis.

International audienceColorectal cancer is the second cause of cancer death inUnited States: precursor lesions (polyps) detection is key for patientsurvival. Though colonoscopy is the gold standard screening tool, somepolyps are still missed. Several computational systems have been pro-posed but none of them are used in the clinical room mainly due to com-putational constraints. Besides, most of them are built over still framedatabases, decreasing their performance on video analysis due to the lackof output stability and not coping with associated variability on imagequality and polyp appearance. We propose a strategy to adapt thesemethods to video analysis by adding a spatio-temporal stability mod-ule and studying a combination of features to capture polyp appearancevariability. We validate our strategy, incorporated on a real-time detec-tion method, on a public video database. Resulting method detects allpolyps under real time constraints, increasing its performance due to ouradaptation strategy

Real-Time Polyp Detection in Colonoscopy Videos.: A Preliminary Study For Adapting Still Frame-basedMethodology To Video Sequences Analysis

International audiencePurpose: Colorectal cancer is the second leading cause of cancerdeath in United States when men and women are combined. Its incidence canbe mitigated by detecting its precursor lesion, the polyp, before it developsinto cancer. Colonoscopy is still the gold standard for colon screening thoughsome polyps are still missed. Several computational systems have already beenproposed to assist clinicians in this task but none of them is actually used inthe exploration room due to not meeting real time constraints and not beingtested under actual interventional sequences, compulsory to being of actualclinical use. Method: We present in this paper a real time polyp detectionmethod built by adapting an existing frame{learning-based detection systemto full sequences analysis; adaptation involves the use of more computationallyecient feature descriptors and the incorporation of spatio-temporal stabilityin method's response. We validate our methodology over a new fully publicannotated video database and under clinical and technical criteria. ResultsResults show that our approach is able to detect all dierent polyps in the18 video sequences that were considered while meeting real time constraints.More precisely, we study the impact of the choice of local feature descriptor(LBP and Haar) in the overall performance when considering usual metrics and how performance can be improved by considering a strengthening strategyin the learning process, by using spatio-temporal coherence and by combiningdierent types of local features. ConclusionWork presented in this paper showsa strategy to adapt a still-frame-based polyp detection to video analysis. Byanalyzing the performance of our system we have also discovered potentialfuture improvements related to the preprocessing of the frames extracted fromthe video. We also provide a full methodology to assess performance of a givenmethod considering both clinical usability metrics and more usual ones inmachine learning

Dielectric properties of colon polyps, cancer, and normal mucosa: Ex vivo measurements from 0.5 to 20 GHz

Purpose: Colorectal cancer is highly preventable by detecting and removing polyps, which are the precursors. Currently, the most accurate test is colonoscopy, but still misses 22% of polyps due to visualization limitations. In this paper, we preliminary assess the potential of microwave imaging and dielectric properties (e.g., complex permittivity) as a complementary method for detecting polyps and cancer tissue in the colon. The dielectric properties of biological tissues have been used in a wide variety of applications, including safety assessment of wireless technologies and design of medical diagnostic or therapeutic techniques (microwave imaging, hyperthermia, and ablation). The main purpose of this work is to measure the complex permittivity of different types of colon polyps, cancer, and normal mucosa in ex vivo human samples to study if the dielectric properties are appropriate for classification purposes. Methods: The complex permittivity of freshly excised healthy colon tissue, cancer, and histological samples of different types of polyps from 23 patients was characterized using an open-ended coaxial probe between 0.5 and 20 GHz. The obtained measurements were classified into five tissue groups before applying a data reduction step with a frequency dispersive single-pole Debye model. The classification was finally compared with pathological analysis of tissue samples, which is the gold standard. Results: The complex permittivity progressively increases as the tissue degenerates from normal to cancer. When comparing to the gold-standard histological tissue analysis, the sensitivity and specificity of the proposed method is the following: 100% and 95% for cancer diagnosis; 91% and 62% for adenomas with high-grade dysplasia; 100% and 61% for adenomas with low-grade dysplasia; and 100% and 74% for hyperplastic polyps, respectively. In addition, complex permittivity measurements were independent of the lesion shape and size, which is also an interesting property comparing to current colonoscopy techniques. Conclusions: The contrast in complex permittivities between normal and abnormal colon tissues presented here for the first time demonstrate the potential of these measurements for tissue classification. It also opens the door to the development of a microwave endoscopic device to complement the outcomes of colonoscopy with functional tissue information.This work was supported by the Department of Universitats, Recerca i Societat de la Informacio of the Catalan Government through Llavor 2014LLAV00016 and producte 2016PROD00068 projects, La Caixa through Caixaimpulse program CI16-00058, the Spanish Comision Interministerial de Ciencia y Tecnologıa (CICYT) under projects TEC2016- 78028-C3-1-P, TEC2014-58582-R, the Spanish Agencia Estatal de Investigacion Unidad de Excelencia Maria de Maeztu (MDM-2016-0600), the Spanish Ministerio de Economıa, Industria y Competitividad, DTS17/00090 and CERCA Programme/Generalitat de Catalunya