30 research outputs found
Segmentation of 3D pore space from CT images using curvilinear skeleton: application to numerical simulation of microbial decomposition
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
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.
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
์์์ ๊ธฐ ํฅ์์ ์ํ ๋ฅ๋ฌ๋ ๊ธฐ๋ฒ ์ฐ๊ตฌ: ๋์ฅ๋ด์๊ฒฝ ์ง๋จ ๋ฐ ๋ก๋ด์์ ์ ๊ธฐ ํ๊ฐ์ ์ ์ฉ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํ๋๊ณผ์ ์์ฉ์์ฒด๊ณตํ์ ๊ณต, 2020. 8. ๊นํฌ์ฐฌ.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 ์๋ฃ์ง์ ์์์ ๊ธฐ ๋ฅ๋ ฅ์ ํฅ์์ํค๊ธฐ ์ํ์ฌ ์๋ก์ด ๋ฅ๋ฌ๋ ๊ธฐ๋ฒ๋ค์ ์ ์ํ๊ณ ๋ค์ ๋ ๊ฐ์ง ์ค๋ก์ ๋ํด ์ ์ฉํ์ฌ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ํ๊ฐํ์๋ค.
์ฒซ ๋ฒ์งธ ์ฐ๊ตฌ์์๋ ๋์ฅ๋ด์๊ฒฝ์ผ๋ก ๊ดํ ์ง๋จ ์, ๋ด์๊ฒฝ ์ ๋ฌธ์์ ์ง๋จ ๋ฅ๋ ฅ์ ํฅ์์ํค๊ธฐ ์ํ์ฌ ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ์ ์ฉ์ข
๋ถ๋ฅ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๋ฐํ๊ณ , ๋ด์๊ฒฝ ์ ๋ฌธ์์ ์ง๋จ ๋ฅ๋ ฅ ํฅ์ ์ฌ๋ถ๋ฅผ ๊ฒ์ฆํ๊ณ ์ ํ์๋ค. ๋์ฅ๋ด์๊ฒฝ ๊ฒ์ฌ๋ก ์์ข
์ผ๋ก ์ฆ์ํ ์ ์๋ ์ ์ข
๊ณผ ๊ณผ์ฆ์์ฑ ์ฉ์ข
์ ์ง๋จํ๋ ๊ฒ์ ์ค์ํ๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ํ๋์ญ ์์ ๋ด์๊ฒฝ์ผ๋ก ์ดฌ์ํ ๋์ฅ ์ฉ์ข
์์์ผ๋ก ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง์ ํ์ตํ์ฌ ๋ถ๋ฅ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๋ฐํ์๋ค. ์ ์ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์๋ ๊ธฐ๊ณํ์ต (AutoML) ๋ฐฉ๋ฒ์ผ๋ก, ๋์ฅ ์ฉ์ข
์์์ ์ต์ ํ๋ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ์ฐพ๊ณ ์ ๊ฒฝ๋ง์ ๊ฐ์ค์น๋ฅผ ํ์ตํ์๋ค. ๋ํ ๊ธฐ์ธ๊ธฐ-๊ฐ์ค์น ํด๋์ค ํ์ฑํ ๋งตํ ๊ธฐ๋ฒ์ ์ด์ฉํ์ฌ ๊ฐ๋ฐํ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง ๊ฒฐ๊ณผ์ ํ๋ฅ ์ ๊ทผ๊ฑฐ๋ฅผ ์ฉ์ข
์์น์ ์๊ฐ์ ์ผ๋ก ๋ํ๋๋๋ก ํจ์ผ๋ก ๋ด์๊ฒฝ ์ ๋ฌธ์์ ์ง๋จ์ ๋๋๋ก ํ์๋ค. ๋ง์ง๋ง์ผ๋ก, ์๋ จ๋ ๊ทธ๋ฃน๋ณ๋ก ๋ด์๊ฒฝ ์ ๋ฌธ์๊ฐ ์ฉ์ข
๋ถ๋ฅ ์๊ณ ๋ฆฌ์ฆ์ ๊ฒฐ๊ณผ๋ฅผ ์ฐธ๊ณ ํ์์ ๋ ์ง๋จ ๋ฅ๋ ฅ์ด ํฅ์๋์๋์ง ๋น๊ต ์คํ์ ์งํํ์๊ณ , ๋ชจ๋ ๊ทธ๋ฃน์์ ์ ์๋ฏธํ๊ฒ ์ง๋จ ์ ํ๋๊ฐ ํฅ์๋๊ณ ์ง๋จ ์๊ฐ์ด ๋จ์ถ๋์์์ ํ์ธํ์๋ค.
๋ ๋ฒ์งธ ์ฐ๊ตฌ์์๋ ๋ก๋ด์์ ๋์์์์ ์์ ๋๊ตฌ ์์น ์ถ์ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๋ฐํ๊ณ , ํ๋ํ ์์ ๋๊ตฌ์ ์์ง์ ์ ๋ณด๋ฅผ ๋ฐํ์ผ๋ก ์์ ์์ ์๋ จ๋๋ฅผ ์ ๋์ ์ผ๋ก ํ๊ฐํ๋ ๋ชจ๋ธ์ ์ ์ํ์๋ค. ์์ ๋๊ตฌ์ ์์ง์์ ์์ ์์ ๋ก๋ด์์ ์๋ จ๋๋ฅผ ํ๊ฐํ๊ธฐ ์ํ ์ฃผ์ํ ์ ๋ณด์ด๋ค. ๋ฐ๋ผ์ ๋ณธ ์ฐ๊ตฌ๋ ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ์ ์๋ ์์ ๋๊ตฌ ์ถ์ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๋ฐํ์์ผ๋ฉฐ, ๋ค์ ๋๊ฐ์ง ์ ํ์ฐ๊ตฌ์ ํ๊ณ์ ์ ๊ทน๋ณตํ์๋ค. ์ธ์คํด์ค ๋ถํ (Instance Segmentation) ํ๋ ์์์ ๊ฐ๋ฐํ์ฌ ํ์ (Occlusion) ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ์๊ณ , ์ถ์ ๊ธฐ (Tracker)์ ์ฌ์๋ณํ (Re-Identification) ์๊ณ ๋ฆฌ์ฆ์ผ๋ก ๊ตฌ์ฑ๋ ์ถ์ ํ๋ ์์์ ๊ฐ๋ฐํ์ฌ ๋์์์์ ์ถ์ ํ๋ ์์ ๋๊ตฌ์ ์ข
๋ฅ๊ฐ ์ ์ง๋๋๋ก ํ์๋ค. ๋ํ ๋ก๋ด์์ ๋์์์ ํน์์ฑ์ ๊ณ ๋ คํ์ฌ ์์ ๋๊ตฌ์ ์์ง์์ ํ๋ํ๊ธฐ์ํด ์์ ๋๊ตฌ ๋ ์์น์ ๋ก๋ด ํ-์ธ๋์ผ์ดํฐ (Arm-Indicator) ์ธ์ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๋ฐํ์๋ค. ์ ์ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ฑ๋ฅ์ ์์ธกํ ์์ ๋๊ตฌ ๋ ์์น์ ์ ๋ต ์์น ๊ฐ์ ํ๊ท ์ ๊ณฑ๊ทผ ์ค์ฐจ, ๊ณก์ ์๋ ๋ฉด์ , ํผ์ด์จ ์๊ด๋ถ์์ผ๋ก ํ๊ฐํ์๋ค. ๋ง์ง๋ง์ผ๋ก, ์์ ๋๊ตฌ์ ์์ง์์ผ๋ก๋ถํฐ ์์ง์ ์งํ๋ฅผ ๊ณ์ฐํ๊ณ ์ด๋ฅผ ๋ฐํ์ผ๋ก ๊ธฐ๊ณํ์ต ๊ธฐ๋ฐ์ ๋ก๋ด์์ ์๋ จ๋ ํ๊ฐ ๋ชจ๋ธ์ ๊ฐ๋ฐํ์๋ค. ๊ฐ๋ฐํ ํ๊ฐ ๋ชจ๋ธ์ ๊ธฐ์กด์ Objective Structured Assessment of Technical Skill (OSATS), Global Evaluative Assessment of Robotic Surgery (GEARS) ํ๊ฐ ๋ฐฉ๋ฒ๊ณผ ์ ์ฌํ ์ฑ๋ฅ์ ๋ณด์์ ํ์ธํ์๋ค.
๋ณธ ๋
ผ๋ฌธ์ ์๋ฃ์ง์ ์์์ ๊ธฐ ๋ฅ๋ ฅ์ ํฅ์์ํค๊ธฐ ์ํ์ฌ ๋์ฅ ์ฉ์ข
์์๊ณผ ๋ก๋ด์์ ๋์์์ ๋ฅ๋ฌ๋ ๊ธฐ์ ์ ์ ์ฉํ๊ณ ๊ทธ ์ ํจ์ฑ์ ํ์ธํ์์ผ๋ฉฐ, ํฅํ์ ์ ์ํ๋ ๋ฐฉ๋ฒ์ด ์์์์ ์ฌ์ฉ๋๊ณ ์๋ ์ง๋จ ๋ฐ ํ๊ฐ ๋ฐฉ๋ฒ์ ๋์์ด ๋ ๊ฒ์ผ๋ก ๊ธฐ๋ํ๋ค.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
LIPIcs, Volume 258, SoCG 2023, Complete Volum
Novel methodologies and technologies for the multiscale and multimodal study of Autism Spectrum Disorders (ASDs)
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