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

AeroPath: An airway segmentation benchmark dataset with challenging pathology

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. However, the ATM'22 dataset includes few patients with severe pathologies affecting the airway tree anatomy. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available.Comment: 13 pages, 5 figures, submitted to Scientific Report

Parallel centerline extraction on the GPU

Centerline extraction is important in a variety of visualization applications including shape analysis, geometry processing, and virtual endoscopy. Centerlines allow accurate measurements of length along winding tubular structures, assist automatic virtual navigation, and provide a path-planning system to control the movement and orientation of a virtual camera. However, efficiently computing centerlines with the desired accuracy has been a major challenge. Existing centerline methods are either not fast enough or not accurate enough for interactive application to complex 3D shapes. Some methods based on distance mapping are accurate, but these are sequential algorithms which have limited performance when running on the CPU. To our knowledge, there is no accurate parallel centerline algorithm that can take advantage of modern many-core parallel computing resources, such as GPUs, to perform automatic centerline extraction from large data volumes at interactive speed and with high accuracy. In this paper, we present a new parallel centerline extraction algorithm suitable for implementation on a GPU to produce highly accurate, 26-connected, one-voxel-thick centerlines at interactive speed. The resulting centerlines are as accurate as those produced by a state-of-the-art sequential CPU method [40], while being computed hundreds of times faster. Applications to fly through path planning and virtual endoscopy are discussed. Experimental results demonstrating centeredness, robustness and efficiency are presented

Digital Eversion of a Hollow Structure: An Application in Virtual Colonography

A new methodology is presented for digital eversion of a hollow structure. The digital eversion is advantageous for better visualization of a larger portion of the inner surface with preservation of geometric relationship and without time-consuming navigation. Together with other techniques, digital eversion may help improve screening, diagnosis, surgical planning, and medical education. Two eversion algorithms are proposed and evaluated in numerical simulation to demonstrate the feasibility of the approach

Pre-clinical validation of virtual bronchoscopy using 3D Slicer

Lung cancer still represents the leading cause of cancer-related death, and the long-term survival rate remains low. Computed tomography (CT) is currently the most common imaging modality for lung diseases recognition. The purpose of this work was to develop a simple and easily accessible virtual bronchoscopy system to be coupled with a customized electromagnetic (EM) tracking system for navigation in the lung and which requires as little user interaction as possible, while maintaining high usability. The proposed method has been implemented as an extension to the open-source platform, 3D Slicer. It creates a virtual reconstruction of the airways starting from CT images for virtual navigation. It provides tools for pre-procedural planning and virtual navigation, and it has been optimized for use in combination with a of freedom EM tracking sensor. Performance of the algorithm has been evaluated in ex vivo and in vivo testing. During ex vivo testing, nine volunteer physicians tested the implemented algorithm to navigate three separate targets placed inside a breathing pig lung model. In general, the system proved easy to use and accurate in replicating the clinical setting and seemed to help choose the correct path without any previous experience or image analysis. Two separate animal studies confirmed technical feasibility and usability of the system. This work describes an easily accessible virtual bronchoscopy system for navigation in the lung. The system provides the user with a complete set of tools that facilitate navigation towards user-selected regions of interest. Results from ex vivo and in vivo studies showed that the system opens the way for potential future work with virtual navigation for safe and reliable airway disease diagnosis

Segmentation of distal airways using structural analysis

Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons