110 research outputs found

    PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets

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    Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for e_ective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four di_erent models, obtained by combining two backbones and two encoderโ€“decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.This work was partially supported by PICCOLO project. This project has received funding from the European Unionโ€™s Horizon2020 research and innovation programme under grant agreement No 732111. Furthermore, this publication has also been partially supported by GR18199 from Consejerรญa de Economรญa, Ciencia y Agenda Digital of Junta de Extremadura (co-funded by European Regional Development Fundโ€“ERDF. โ€œA way to make Europeโ€/ โ€œInvesting in your futureโ€. This work has been performed by the ICTS โ€œNANBIOSISโ€ at the Jesรบs Usรณn Minimally Invasive Surgery Centre

    Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

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    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

    ์ž„์ƒ์ˆ ๊ธฐ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฒ• ์—ฐ๊ตฌ: ๋Œ€์žฅ๋‚ด์‹œ๊ฒฝ ์ง„๋‹จ ๋ฐ ๋กœ๋ด‡์ˆ˜์ˆ  ์ˆ ๊ธฐ ํ‰๊ฐ€์— ์ ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์˜์šฉ์ƒ์ฒด๊ณตํ•™์ „๊ณต, 2020. 8. ๊น€ํฌ์ฐฌ.This paper presents deep learning-based methods for improving performance of clinicians. Novel methods were applied to the following two clinical cases and the results were evaluated. In the first study, a deep learning-based polyp classification algorithm for improving clinical performance of endoscopist during colonoscopy diagnosis was developed. Colonoscopy is the main method for diagnosing adenomatous polyp, which can multiply into a colorectal cancer and hyperplastic polyps. The classification algorithm was developed using convolutional neural network (CNN), trained with colorectal polyp images taken by a narrow-band imaging colonoscopy. The proposed method is built around an automatic machine learning (AutoML) which searches for the optimal architecture of CNN for colorectal polyp image classification and trains the weights of the architecture. In addition, gradient-weighted class activation mapping technique was used to overlay the probabilistic basis of the prediction result on the polyp location to aid the endoscopists visually. To verify the improvement in diagnostic performance, the efficacy of endoscopists with varying proficiency levels were compared with or without the aid of the proposed polyp classification algorithm. The results confirmed that, on average, diagnostic accuracy was improved and diagnosis time was shortened in all proficiency groups significantly. In the second study, a surgical instruments tracking algorithm for robotic surgery video was developed, and a model for quantitatively evaluating the surgeons surgical skill based on the acquired motion information of the surgical instruments was proposed. The movement of surgical instruments is the main component of evaluation for surgical skill. Therefore, the focus of this study was develop an automatic surgical instruments tracking algorithm, and to overcome the limitations presented by previous methods. The instance segmentation framework was developed to solve the instrument occlusion issue, and a tracking framework composed of a tracker and a re-identification algorithm was developed to maintain the type of surgical instruments being tracked in the video. In addition, algorithms for detecting the tip position of instruments and arm-indicator were developed to acquire the movement of devices specialized for the robotic surgery video. The performance of the proposed method was evaluated by measuring the difference between the predicted tip position and the ground truth position of the instruments using root mean square error, area under the curve, and Pearsons correlation analysis. Furthermore, motion metrics were calculated from the movement of surgical instruments, and a machine learning-based robotic surgical skill evaluation model was developed based on these metrics. These models were used to evaluate clinicians, and results were similar in the developed evaluation models, the Objective Structured Assessment of Technical Skill (OSATS), and the Global Evaluative Assessment of Robotic Surgery (GEARS) evaluation methods. In this study, deep learning technology was applied to colorectal polyp images for a polyp classification, and to robotic surgery videos for surgical instruments tracking. The improvement in clinical performance with the aid of these methods were evaluated and verified.๋ณธ ๋…ผ๋ฌธ์€ ์˜๋ฃŒ์ง„์˜ ์ž„์ƒ์ˆ ๊ธฐ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฒ•๋“ค์„ ์ œ์•ˆํ•˜๊ณ  ๋‹ค์Œ ๋‘ ๊ฐ€์ง€ ์‹ค๋ก€์— ๋Œ€ํ•ด ์ ์šฉํ•˜์—ฌ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋Œ€์žฅ๋‚ด์‹œ๊ฒฝ์œผ๋กœ ๊ด‘ํ•™ ์ง„๋‹จ ์‹œ, ๋‚ด์‹œ๊ฒฝ ์ „๋ฌธ์˜์˜ ์ง„๋‹จ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์šฉ์ข… ๋ถ„๋ฅ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๊ณ , ๋‚ด์‹œ๊ฒฝ ์ „๋ฌธ์˜์˜ ์ง„๋‹จ ๋Šฅ๋ ฅ ํ–ฅ์ƒ ์—ฌ๋ถ€๋ฅผ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋Œ€์žฅ๋‚ด์‹œ๊ฒฝ ๊ฒ€์‚ฌ๋กœ ์•”์ข…์œผ๋กœ ์ฆ์‹ํ•  ์ˆ˜ ์žˆ๋Š” ์„ ์ข…๊ณผ ๊ณผ์ฆ์‹์„ฑ ์šฉ์ข…์„ ์ง„๋‹จํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ˜‘๋Œ€์—ญ ์˜์ƒ ๋‚ด์‹œ๊ฒฝ์œผ๋กœ ์ดฌ์˜ํ•œ ๋Œ€์žฅ ์šฉ์ข… ์˜์ƒ์œผ๋กœ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง์„ ํ•™์Šตํ•˜์—ฌ ๋ถ„๋ฅ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ž๋™ ๊ธฐ๊ณ„ํ•™์Šต (AutoML) ๋ฐฉ๋ฒ•์œผ๋กœ, ๋Œ€์žฅ ์šฉ์ข… ์˜์ƒ์— ์ตœ์ ํ™”๋œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ์ฐพ๊ณ  ์‹ ๊ฒฝ๋ง์˜ ๊ฐ€์ค‘์น˜๋ฅผ ํ•™์Šตํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ธฐ์šธ๊ธฐ-๊ฐ€์ค‘์น˜ ํด๋ž˜์Šค ํ™œ์„ฑํ™” ๋งตํ•‘ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๊ฐœ๋ฐœํ•œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๊ฒฐ๊ณผ์˜ ํ™•๋ฅ ์  ๊ทผ๊ฑฐ๋ฅผ ์šฉ์ข… ์œ„์น˜์— ์‹œ๊ฐ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋„๋ก ํ•จ์œผ๋กœ ๋‚ด์‹œ๊ฒฝ ์ „๋ฌธ์˜์˜ ์ง„๋‹จ์„ ๋•๋„๋ก ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ˆ™๋ จ๋„ ๊ทธ๋ฃน๋ณ„๋กœ ๋‚ด์‹œ๊ฒฝ ์ „๋ฌธ์˜๊ฐ€ ์šฉ์ข… ๋ถ„๋ฅ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฒฐ๊ณผ๋ฅผ ์ฐธ๊ณ ํ•˜์˜€์„ ๋•Œ ์ง„๋‹จ ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋˜์—ˆ๋Š”์ง€ ๋น„๊ต ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๊ณ , ๋ชจ๋“  ๊ทธ๋ฃน์—์„œ ์œ ์˜๋ฏธํ•˜๊ฒŒ ์ง„๋‹จ ์ •ํ™•๋„๊ฐ€ ํ–ฅ์ƒ๋˜๊ณ  ์ง„๋‹จ ์‹œ๊ฐ„์ด ๋‹จ์ถ•๋˜์—ˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋กœ๋ด‡์ˆ˜์ˆ  ๋™์˜์ƒ์—์„œ ์ˆ˜์ˆ ๋„๊ตฌ ์œ„์น˜ ์ถ”์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๊ณ , ํš๋“ํ•œ ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์›€์ง์ž„ ์ •๋ณด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ˆ˜์ˆ ์ž์˜ ์ˆ™๋ จ๋„๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์›€์ง์ž„์€ ์ˆ˜์ˆ ์ž์˜ ๋กœ๋ด‡์ˆ˜์ˆ  ์ˆ™๋ จ๋„๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ์ฃผ์š”ํ•œ ์ •๋ณด์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์ž๋™ ์ˆ˜์ˆ ๋„๊ตฌ ์ถ”์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜์˜€์œผ๋ฉฐ, ๋‹ค์Œ ๋‘๊ฐ€์ง€ ์„ ํ–‰์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์ ์„ ๊ทน๋ณตํ•˜์˜€๋‹ค. ์ธ์Šคํ„ด์Šค ๋ถ„ํ•  (Instance Segmentation) ํ”„๋ ˆ์ž„์›์„ ๊ฐœ๋ฐœํ•˜์—ฌ ํ์ƒ‰ (Occlusion) ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์˜€๊ณ , ์ถ”์ ๊ธฐ (Tracker)์™€ ์žฌ์‹๋ณ„ํ™” (Re-Identification) ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ๊ตฌ์„ฑ๋œ ์ถ”์  ํ”„๋ ˆ์ž„์›์„ ๊ฐœ๋ฐœํ•˜์—ฌ ๋™์˜์ƒ์—์„œ ์ถ”์ ํ•˜๋Š” ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์ข…๋ฅ˜๊ฐ€ ์œ ์ง€๋˜๋„๋ก ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋กœ๋ด‡์ˆ˜์ˆ  ๋™์˜์ƒ์˜ ํŠน์ˆ˜์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์›€์ง์ž„์„ ํš๋“ํ•˜๊ธฐ์œ„ํ•ด ์ˆ˜์ˆ ๋„๊ตฌ ๋ ์œ„์น˜์™€ ๋กœ๋ด‡ ํŒ”-์ธ๋””์ผ€์ดํ„ฐ (Arm-Indicator) ์ธ์‹ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์€ ์˜ˆ์ธกํ•œ ์ˆ˜์ˆ ๋„๊ตฌ ๋ ์œ„์น˜์™€ ์ •๋‹ต ์œ„์น˜ ๊ฐ„์˜ ํ‰๊ท  ์ œ๊ณฑ๊ทผ ์˜ค์ฐจ, ๊ณก์„  ์•„๋ž˜ ๋ฉด์ , ํ”ผ์–ด์Šจ ์ƒ๊ด€๋ถ„์„์œผ๋กœ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์›€์ง์ž„์œผ๋กœ๋ถ€ํ„ฐ ์›€์ง์ž„ ์ง€ํ‘œ๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๋กœ๋ด‡์ˆ˜์ˆ  ์ˆ™๋ จ๋„ ํ‰๊ฐ€ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœํ•œ ํ‰๊ฐ€ ๋ชจ๋ธ์€ ๊ธฐ์กด์˜ Objective Structured Assessment of Technical Skill (OSATS), Global Evaluative Assessment of Robotic Surgery (GEARS) ํ‰๊ฐ€ ๋ฐฉ๋ฒ•๊ณผ ์œ ์‚ฌํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์˜๋ฃŒ์ง„์˜ ์ž„์ƒ์ˆ ๊ธฐ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ๋Œ€์žฅ ์šฉ์ข… ์˜์ƒ๊ณผ ๋กœ๋ด‡์ˆ˜์ˆ  ๋™์˜์ƒ์— ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ์ˆ ์„ ์ ์šฉํ•˜๊ณ  ๊ทธ ์œ ํšจ์„ฑ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ํ–ฅํ›„์— ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์ž„์ƒ์—์„œ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ์ง„๋‹จ ๋ฐ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•์˜ ๋Œ€์•ˆ์ด ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Chapter 1 General Introduction 1 1.1 Deep Learning for Medical Image Analysis 1 1.2 Deep Learning for Colonoscipic Diagnosis 2 1.3 Deep Learning for Robotic Surgical Skill Assessment 3 1.4 Thesis Objectives 5 Chapter 2 Optical Diagnosis of Colorectal Polyps using Deep Learning with Visual Explanations 7 2.1 Introduction 7 2.1.1 Background 7 2.1.2 Needs 8 2.1.3 Related Work 9 2.2 Methods 11 2.2.1 Study Design 11 2.2.2 Dataset 14 2.2.3 Preprocessing 17 2.2.4 Convolutional Neural Networks (CNN) 21 2.2.4.1 Standard CNN 21 2.2.4.2 Search for CNN Architecture 22 2.2.4.3 Searched CNN Training 23 2.2.4.4 Visual Explanation 24 2.2.5 Evaluation of CNN and Endoscopist Performances 25 2.3 Experiments and Results 27 2.3.1 CNN Performance 27 2.3.2 Results of Visual Explanation 31 2.3.3 Endoscopist with CNN Performance 33 2.4 Discussion 45 2.4.1 Research Significance 45 2.4.2 Limitations 47 2.5 Conclusion 49 Chapter 3 Surgical Skill Assessment during Robotic Surgery by Deep Learning-based Surgical Instrument Tracking 50 3.1 Introduction 50 3.1.1 Background 50 3.1.2 Needs 51 3.1.3 Related Work 52 3.2 Methods 56 3.2.1 Study Design 56 3.2.2 Dataset 59 3.2.3 Instance Segmentation Framework 63 3.2.4 Tracking Framework 66 3.2.4.1 Tracker 66 3.2.4.2 Re-identification 68 3.2.5 Surgical Instrument Tip Detection 69 3.2.6 Arm-Indicator Recognition 71 3.2.7 Surgical Skill Prediction Model 71 3.3 Experiments and Results 78 3.3.1 Performance of Instance Segmentation Framework 78 3.3.2 Performance of Tracking Framework 82 3.3.3 Evaluation of Surgical Instruments Trajectory 83 3.3.4 Evaluation of Surgical Skill Prediction Model 86 3.4 Discussion 90 3.4.1 Research Significance 90 3.4.2 Limitations 92 3.5 Conclusion 96 Chapter 4 Summary and Future Works 97 4.1 Thesis Summary 97 4.2 Limitations and Future Works 98 Bibliography 100 Abstract in Korean 116 Acknowledgement 119Docto

    Deep Learning for Improved Polyp Detection from Synthetic Narrow-Band Imaging

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    To cope with the growing prevalence of colorectal cancer (CRC), screening programs for polyp detection and removal have proven their usefulness. Colonoscopy is considered the best-performing procedure for CRC screening. To ease the examination, deep learning based methods for automatic polyp detection have been developed for conventional white-light imaging (WLI). Compared with WLI, narrow-band imaging (NBI) can improve polyp classification during colonoscopy but requires special equipment. We propose a CycleGAN-based framework to convert images captured with regular WLI to synthetic NBI (SNBI) as a pre-processing method for improving object detection on WLI when NBI is unavailable. This paper first shows that better results for polyp detection can be achieved on NBI compared to a relatively similar dataset of WLI. Secondly, experimental results demonstrate that our proposed modality translation can achieve improved polyp detection on SNBI images generated from WLI compared to the original WLI. This is because our WLI-to-SNBI translation model can enhance the observation of polyp surface patterns in the generated SNBI images

    Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

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    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

    Building up the Future of Colonoscopy โ€“ A Synergy between Clinicians and Computer Scientists

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    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

    Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

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    Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby in-vivo classification of polyps based on enhanced imaging replaces histopathology, has not been incorporated into routine practice because it is limited by interobserver variability and generally only meets accepted standards in expert settings. Real-time decision-support software has been developed to detect and characterise polyps, and also to offer feedback on the technical quality of inspection. Some of the current algorithms, particularly with recent advances in artificial intelligence techniques, match human expert performance for optical biopsy. In this Review, we summarise the evidence for clinical applications of computer-aided diagnosis and artificial intelligence in colonoscopy
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