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

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

Design and evaluation of an antenna applicator for a microwave colonoscopy system

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a design of a compact antenna applicator for a microwave colonoscopy system. Although colonoscopy is the most effective method for colorectal cancer detection, it suffers from important visualization restrictions that limit its performance. We recently reported that the contrast between healthy mucosa and cancer was 30%-100% for the relative permittivity and conductivity, respectively, at 8 GHz, and the complex permittivity increased proportionally to the degeneration rate of polyps (cancer precursors). The applicator is designed as a compact cylindrical array of eight antennas attached at the tip of a conventional colonoscope. The design presented here is a proof-of-concept applicator composed by one transmitting and one receiving cavity-backed U-shaped slot antenna elements fed by an L-shaped microstrip line. The antennas are low profile and present a high isolation at 8 GHz. The antenna performance is assessed with simulations and experimentally with a phantom composed by different liquids.Peer ReviewedPostprint (author's final draft

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

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

This is the accepted version of the following article: Guardiola, M. , Buitrago, S. , Fernández‐Esparrach, G. , O'Callaghan, J. M., Romeu, J. , Cuatrecasas, M. , Córdova, H. , González Ballester, M. Á. and Camara, O. (2018), Dielectric properties of colon polyps, cancer, and normal mucosa: Ex vivo measurements from 0.5 to 20 GHz. Med. Phys., 45: 3768-3782. doi:10.1002/mp.13016, which has been published in final form at https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13016. This article may be used for non-commercial purposes in accordance with the Wiley Self-Archiving Policy [http://olabout.wiley.com/WileyCDA/Section/id-820227.html].Colorectal cancer is highly preventable by detecting and removing polyps, which are the precursors. 20 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 25 (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.Peer ReviewedPostprint (author's final draft

Automatic polyp detection using microwave endoscopy for colorectal cancer prevention and early detection: phantom validation

A system to integrate microwave imaging with optical colonoscopy is presented. The overarching goal is to improve the prevention and early diagnosis of one of the main health and economic burdens of an increasingly aging population, i.e., colorectal cancer. For a colonoscopy, the gold standard for colorectal cancer diagnosis, 22% of polyps are not detected, and the risk of cancer after a negative colonoscopy can be up to 7.9%. To remedy this, a microwave imaging system able to generate an alarm when a polyp is detected is designed, manufactured and validated with a colon phantom composed of tissue-mimicking oil-gelatin materials reproducing the anatomy and dielectric properties of a human colon with a polyp. The acquisition was performed by a miniaturized ring-shaped switched array of 16 antennas attachable at the tip of a conventional colonoscope. This has been conceived to satisfy endoscopy size restrictions, patient safety and intercompatibility with current clinical practice. A Modified Monofocusing imaging method preceded by a previous frame average subtraction as a calibration technique shows a perfect detection of a 10-mm polyp (100% sensitivity and specificity) in the eight analyzed trajectories. The phantom results demonstrate the feasibility of the system in future preclinical trials.The work of Alejandra Garrido was supported by DIN2019-010857. The work of Roberto Sont, Ignasi Belda, and Marta Guardiola was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 960251 and in part by the European Institute of Innovation and Technology (EIT). The work of Jordi Romeu was supported by PID2019-107885GB-C31/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Microwave-Based Colonoscopy: Preclinical Evaluation in an Ex Vivo Human Colon Model

Introduction: Microwave imaging can obtain 360° anatomical and functional images of the colon representing the existing contrast in dielectric properties between different tissues. Microwaves are safe (nonionizing) and have the potential of reducing the visualization problems of conventional colonoscopy. This study assessed the efficacy of a microwave-based colonoscopy device to detect neoplastic lesions in an ex vivo human colon model. Methods: Fresh surgically excised colorectal specimens containing cancer or polyps were fixed to a 3D positioning system, and the accessory device was introduced horizontally inside the ex vivo colon lumen and moved along it simulating a real colonoscopy exploration. Measurements of the colon were taken every 4 mm with the microwave-based colonoscopy device and processed with a microwave imaging algorithm. Results: 14 ex vivo human colorectal specimens with carcinomas (n = 11) or adenomas with high grade dysplasia (n = 3) were examined with a microwave-based device. Using a detection threshold of 2.79 for the dielectric property contrast, all lesions were detected without false positives or false negatives. Conclusions: This study demonstrates the use of a microwave-based device to be used as an accessory of a standard colonoscope to detect neoplastic lesions in surgically excised colorectal specimens

Successful outcomes of a new combined solution of hyaluronic acid, chondroitin sulfate and poloxamer 407 for submucosal injection: animal survival study

Background: and study aims Endoscopic resection requires use of submucosal injection. This study was conducted to assess efficacy and impact on early healing of hyaluronic acid combined with chondroitin sulfate and poloxamer 407 (Ziverel) when used as a solution for submucosal injection. Materials: and methods Prospective and comparative study of gastric endoscopic mucosal resection (EMR) with three groups of two Yorkshire pigs. Six submucosal cushions were created in each animal by injecting 2 mL of Ziverel (Group 1) or succinylated gelatin (SG) (Group 2), enabling 12 EMR in each group. Submucosal cushions were created with Ziverel in Group 3, without resection. Electrosurgery unit settings were the same in all cases. EMR defects and injection sites were marked with clips. The animals were sacrificed 7 days later. EMR specimen size and duration of procedure were recorded. EMR specimens and EMR scars and injection sites were evaluated by a blinded pathologist. Results: We successfully performed 24 EMR (15 en-bloc and 9 piecemeal, without differences between groups 1 and 2). Mean EMR specimen dimensions were significantly larger in Group 1 (median 19 mm, range 6 - 40 vs 16.6 mm, range 5‑25; P = 0.019), without changing the electrocautery unit settings. Blinded histopathologist assessment of EMR specimens showed less fibrosis in the submucosa and a trend to fewer cautery artifacts with Ziverel and did not identify any significant differences in early healing of resection sites. Conclusion: The combination of Ziverel enables EMR and does not negatively affect early healing

In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy

Background and study aims Artificial intelligence is currently able to accurately predict the histology of colorectal polyps. However, systems developed to date use complex optical technologies and have not been tested in vivo. The objective of this study was to evaluate the efficacy of a new deep learning-based optical diagnosis system, ATENEA, in a real clinical setting using only high-definition white light endoscopy (WLE) and to compare its performance with endoscopists. Methods ATENEA was prospectively tested in real life on consecutive polyps detected in colorectal cancer screening colonoscopies at Hospital Clínic. No images were discarded, and only WLE was used. The in vivo ATENEA's prediction (adenoma vs non-adenoma) was compared with the prediction of four staff endoscopists without specific training in optical diagnosis for the study purposes. Endoscopists were blind to the ATENEA output. Histology was the gold standard. Results Ninety polyps (median size: 5 mm, range: 2-25) from 31 patients were included of which 69 (76.7 %) were adenomas. ATENEA correctly predicted the histology in 63 of 69 (91.3 %, 95 % CI: 82 %-97 %) adenomas and 12 of 21 (57.1 %, 95 % CI: 34 %-78 %) non-adenomas while endoscopists made correct predictions in 52 of 69 (75.4 %, 95 % CI: 60 %-85 %) and 20 of 21 (95.2 %, 95 % CI: 76 %-100 %), respectively. The global accuracy was 83.3 % (95 % CI: 74%-90 %) and 80 % (95 % CI: 70 %-88 %) for ATENEA and endoscopists, respectively. Conclusion ATENEA can accurately be used for in vivo characterization of colorectal polyps, enabling the endoscopist to make direct decisions. ATENEA showed a global accuracy similar to that of endoscopists despite an unsatisfactory performance for non-adenomatous lesions

Motorized spiral enteroscopy is effective in patients with prior abdominal surgery.

Background Motorized Spiral Enteroscopy (MSE) reduces procedure time and increases insertion depth into the small bowel; however, there is scarce evidence on factors afecting MSE efcacy. Aims To evaluate diagnostic yield and adverse events of MSE including patients with prior major abdominal surgery. Methods A prospective observational study was conducted on patients undergoing MSE from June 2019 to December 2021. Demographic characteristics, procedure time, depth of maximum insertion (DMI), technical success, diagnostic yield, and adverse events were collected. Results Seventy-four anterograde (54.4%) and 62 retrograde (45.6%) enteroscopies were performed in 117 patients (64 males, median age 67 years). Fifty patients (42.7%) had prior major abdominal surgery. Technical success was 91.9% for anterograde and 90.3% for retrograde route. Diagnostic yield was 71.6% and 61.3%, respectively. The median DMI was 415 cm (264–585) for anterograde and 120 cm (37–225) for retrograde enteroscopy. In patients with prior major abdominal surgery, MSE showed signifcantly longer small bowel insertion time (38 vs 29 min, p=0.004), with similar diagnostic yield (61 vs 71.4%, p=0.201) and DMI (315 vs 204 cm, p=0.226). The overall adverse event rate was 10.3% (SAE 1.5%), with no diferences related to prior abdominal surgery (p=0.598). Patients with prior surgeries directly involving the gastrointestinal tract showed lower DMI (189 vs 374 cm, p=0.019) with equal exploration time (37.5 vs 38 min, p=0.642) compared to those with other abdominal surgeries. Conclusions MSE is efective and safe in patients with major abdominal surgery, although longer procedure times were observed. A lower depth of insertion was detected in patients with gastrointestinal surgery