30 research outputs found

    Segmentation of 3D pore space from CT images using curvilinear skeleton: application to numerical simulation of microbial decomposition

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    Recent advances in 3D X-ray Computed Tomographic (CT) sensors have stimulated research efforts to unveil the extremely complex micro-scale processes that control the activity of soil microorganisms. Voxel-based description (up to hundreds millions voxels) of the pore space can be extracted, from grey level 3D CT scanner images, by means of simple image processing tools. Classical methods for numerical simulation of biological dynamics using mesh of voxels, such as Lattice Boltzmann Model (LBM), are too much time consuming. Thus, the use of more compact and reliable geometrical representations of pore space can drastically decrease the computational cost of the simulations. Several recent works propose basic analytic volume primitives (e.g. spheres, generalized cylinders, ellipsoids) to define a piece-wise approximation of pore space for numerical simulation of draining, diffusion and microbial decomposition. Such approaches work well but the drawback is that it generates approximation errors. In the present work, we study another alternative where pore space is described by means of geometrically relevant connected subsets of voxels (regions) computed from the curvilinear skeleton. Indeed, many works use the curvilinear skeleton (3D medial axis) for analyzing and partitioning 3D shapes within various domains (medicine, material sciences, petroleum engineering, etc.) but only a few ones in soil sciences. Within the context of soil sciences, most studies dealing with 3D medial axis focus on the determination of pore throats. Here, we segment pore space using curvilinear skeleton in order to achieve numerical simulation of microbial decomposition (including diffusion processes). We validate simulation outputs by comparison with other methods using different pore space geometrical representations (balls, voxels).Comment: preprint, submitted to Computers & Geosciences 202

    Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

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    The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

    Automatic extraction of bronchus and centerline determination from CT images for three dimensional virtual bronchoscopy.

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    Law Tsui Ying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 64-70).Abstracts in English and Chinese.Acknowledgments --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Structure of Bronchus --- p.3Chapter 1.2 --- Existing Systems --- p.4Chapter 1.2.1 --- Virtual Endoscope System (VES) --- p.4Chapter 1.2.2 --- Virtual Reality Surgical Simulator --- p.4Chapter 1.2.3 --- Automated Virtual Colonoscopy (AVC) --- p.5Chapter 1.2.4 --- QUICKSEE --- p.5Chapter 1.3 --- Organization of Thesis --- p.6Chapter 2 --- Three Dimensional Visualization in Medicine --- p.7Chapter 2.1 --- Acquisition --- p.8Chapter 2.1.1 --- Computed Tomography --- p.8Chapter 2.2 --- Resampling --- p.9Chapter 2.3 --- Segmentation and Classification --- p.9Chapter 2.3.1 --- Segmentation by Thresholding --- p.10Chapter 2.3.2 --- Segmentation by Texture Analysis --- p.10Chapter 2.3.3 --- Segmentation by Region Growing --- p.10Chapter 2.3.4 --- Segmentation by Edge Detection --- p.11Chapter 2.4 --- Rendering --- p.12Chapter 2.5 --- Display --- p.13Chapter 2.6 --- Hazards of Visualization --- p.13Chapter 2.6.1 --- Adding Visual Richness and Obscuring Important Detail --- p.14Chapter 2.6.2 --- Enhancing Details Incorrectly --- p.14Chapter 2.6.3 --- The Picture is not the Patient --- p.14Chapter 2.6.4 --- Pictures-'R'-Us --- p.14Chapter 3 --- Overview of Advanced Segmentation Methodologies --- p.15Chapter 3.1 --- Mathematical Morphology --- p.15Chapter 3.2 --- Recursive Region Search --- p.16Chapter 3.3 --- Active Region Models --- p.17Chapter 4 --- Overview of Centerline Methodologies --- p.18Chapter 4.1 --- Thinning Approach --- p.18Chapter 4.2 --- Volume Growing Approach --- p.21Chapter 4.3 --- Combination of Mathematical Morphology and Region Growing Schemes --- p.22Chapter 4.4 --- Simultaneous Borders Identification Approach --- p.23Chapter 4.5 --- Tracking Approach --- p.24Chapter 4.6 --- Distance Transform Approach --- p.25Chapter 5 --- Automated Extraction of Bronchus Area --- p.27Chapter 5.1 --- Basic Idea --- p.27Chapter 5.2 --- Outline of the Automated Extraction Algorithm --- p.28Chapter 5.2.1 --- Selection of a Start Point --- p.28Chapter 5.2.2 --- Three Dimensional Region Growing Method --- p.29Chapter 5.2.3 --- Optimization of the Threshold Value --- p.29Chapter 5.3 --- Retrieval of Start Point Algorithm Using Genetic Algorithm --- p.29Chapter 5.3.1 --- Introduction to Genetic Algorithm --- p.30Chapter 5.3.2 --- Problem Modeling --- p.31Chapter 5.3.3 --- Algorithm for Determining a Start Point --- p.33Chapter 5.3.4 --- Genetic Operators --- p.33Chapter 5.4 --- Three Dimensional Painting Algorithm --- p.34Chapter 5.4.1 --- Outline of the Three Dimensional Painting Algorithm --- p.34Chapter 5.5 --- Optimization of the Threshold Value --- p.36Chapter 6 --- Automatic Centerline Determination Algorithm --- p.38Chapter 6.1 --- Distance Transformations --- p.38Chapter 6.2 --- End Points Retrieval --- p.41Chapter 6.3 --- Graph Based Centerline Algorithm --- p.44Chapter 7 --- Experiments and Discussion --- p.48Chapter 7.1 --- Experiment of Automated Determination of Bronchus Algorithm --- p.48Chapter 7.2 --- Experiment of Automatic Centerline Determination Algorithm --- p.54Chapter 8 --- Conclusion --- p.62Bibliography --- p.6

    ์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

    LIPIcs, Volume 258, SoCG 2023, Complete Volume

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    LIPIcs, Volume 258, SoCG 2023, Complete Volum

    Novel methodologies and technologies for the multiscale and multimodal study of Autism Spectrum Disorders (ASDs)

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    The aim of this PhD thesis was the development of novel bioengineering tools and methodologies that provide a support in the study of ASDs. ASDs are very heterogeneous disturbs and their abnormalities are present both at local and global level. For this reason a multimodal and multiscale approach was followed. The analysis of microstructure was executed on single Purkinje neurons in culture and on organotypic slices extracted from cerebella of GFP wild-type mice and animal models of ASDs. A methodology for the non-invasive imaging of neurons during their growth was set up and a software called NEMO (NEuron MOrphological analysis tool) for the automatic analysis of morphology and connectivity was developed. Microstructure properties can be inferred also in vivo through the quite recent technique of Diffusion Tensor Imaging (DTI). DTI studies in ASDs are based on the hypothesis that the disorder involves aberrant brain connectivity and disruption of white matter tracts between regions implicated in social functioning. In this study DTI was used to investigate structural abnormalities in the white matter structure of young children with ASDs. Moreover the neurostructural bases of echolalia were investigated. The functionality of the brain was analyzed through Functional Magnetic Resonance Imaging (fMRI) using a novel task based on face processing of human, android and robotic faces. A case-control study was performed in order to study how the face processing network is altered in ASDs and how robots are differently processed in ASDs and control groups. Measurements characterizing physiology and behavior of ASD children were also collected using an innovative platform called FACE-T (FACE-Therapy). FACE-T consists of a specially equipped room in which the child, wearing unobtrusive devices for recording physiological and behavioral data as well as gaze information, can interact with an android (FACE, Facial Automaton for Conveying Emotions) and a therapist. The focus was on ECG, as from the analysis of power spectrum density of ECG it is possible to extract features related to the autonomic nervous system that is correlated with brain functionality. These studies give new insights in the study of ASDs exploring aspects not yet addressed. Moreover the methodologies and tools developed could help in the objective characterization of ASD subjects and in the definition of a personalized therapeutic protocol for each child
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