Validating Pareto Optimal Operation Parameters of Polyp Detection Algorithms for CT Colonography

We evaluated a Pareto front-based multi-objective evolutionary algorithm for optimizing our CT colonography (CTC) computer-aided detection (CAD) system. The system identifies colonic polyps based on curvature and volumetric based features, where a set of thresholds for these features was optimized by the evolutionary algorithm. We utilized a two-fold cross-validation (CV) method to test if the optimized thresholds can be generalized to new data sets. We performed the CV method on 133 patients; each patient had a prone and a supine scan. There were 103 colonoscopically confirmed polyps resulting in 188 positive detections in CTC reading from either the prone or the supine scan or both. In the two-fold CV, we randomly divided the 133 patients into two cohorts. Each cohort was used to obtain the Pareto front by a multi-objective genetic algorithm, where a set of optimized thresholds was applied on the test cohort to get test results. This process was repeated twice so that each cohort was used in the training and testing process once. We averaged the two training Pareto fronts as our final training Pareto front and averaged the test results from the two runs in the CV as our final test results. Our experiments demonstrated that the averaged testing results were close to the mean Pareto front determined from the training process. We conclude that the Pareto front-based algorithm appears to be generalizable to new test data

Using Pareto Fronts to Evaluate Polyp Detection Algorithms for CT Colonography

We evaluate and improve an existing curvature-based region growing algorithm for colonic polyp detection for our CT colonography (CTC) computer-aided detection (CAD) system by using Pareto fronts. The performance of a polyp detection algorithm involves two conflicting objectives, minimizing both false negative (FN) and false positive (FP) detection rates. This problem does not produce a single optimal solution but a set of solutions known as a Pareto front. Any solution in a Pareto front can only outperform other solutions in one of the two competing objectives. Using evolutionary algorithms to find the Pareto fronts for multi-objective optimization problems has been common practice for years. However, they are rarely investigated in any CTC CAD system because the computation cost is inherently expensive. To circumvent this problem, we have developed a parallel program implemented on a Linux cluster environment. A data set of 56 CTC colon surfaces with 87 proven positive detections of polyps sized 4 to 60 mm is used to evaluate an existing one-step, and derive a new two-step region growing algorithm. We use a popular algorithm, the Strength Pareto Evolutionary Algorithm (SPEA2), to find the Pareto fronts. The performance differences are evaluated using a statistical approach. The new algorithm outperforms the old one in 81.6% of the sampled Pareto fronts from 20 simulations. When operated at a suitable sensitivity level such as 90.8% (79/87) or 88.5% (77/87), the FP rate is decreased by 24.4% or 45.8% respectively

Efficiency in colonoscopy

Global trends, including demographic changes, are significantly increasing the demand and cost of healthcare. Endoscopy services are no exception and, even before the Covid-19 pandemic, significant pressure resulted in many units failing to meet cancer wait targets. The need to improve efficiency has never been greater and particularly so for colonoscopy which significantly reduces morbidity and mortality from colorectal cancer. Today, advances in colonoscope technologies and emergence of artificial intelligence offer the potential for improved colonoscopy practice. The aim of this thesis is to explore how efficiency in colonoscopy can be enhanced throughout the patient pathway. Five major studies were performed evaluating bowel preparation (CLEANSE), polyp detection (AI-DETECT), optical diagnosis (DISCARD3), insertion technique (WAVE) and post-colonoscopy colorectal cancer (AI-DETECT). CLEANSE is an evaluation of a novel low-volume same-day bowel preparation regime (Plenvu) and showed this offers a more efficient bowel cleansing option than standard regimens. AI-DETECT is a randomised study evaluating a computer-aided detection (CADe) system (GI Genius) and showed a borderline significant improvement in polyp detection is achieved amongst high performing endoscopists. DISCARD3 is a major evaluation of optical diagnosis with a “resect and discard” strategy exploring the learning curve, quality assurance process, causes of error and economic impact. This study shows such a strategy is feasible and safe and could potentially be implemented with a quality assurance process in place within the English Bowel Cancer Screening Progamme (BCSP). WAVE is a randomised study evaluating colonoscopy insertion technique. This showed a ‘hybrid’ insertion technique is more efficient than a water-exchange colonoscopy technique. REFLECT is a retrospective evaluation of post-colonoscopy colorectal cancer cases identified at national level and showed after local root cause analysis a significant proportion were in fact detected cancers. These studies provide valuable insights that we hope will ultimately lead to more efficient colonoscopy whilst maintaining quality and enhancing patient care.Open Acces

Parameter Optimization for Image Denoising Based on Block Matching and 3D Collaborative Filtering

Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation

Artificial Intelligence and Medicine

The introduction of artificial intelligence (AI) has resulted in numerous technological advancements in the medical profession and a radical transformation of the old medical model. Artificial intelligence in medicine consists mostly of machine learning, deep learning, expert systems, intelligent robotics, the internet of medical things, and other prevalent and new AI technology. The primary applications of AI in the medical industry are intelligent screening, intelligent diagnosis, risk prediction, and supplemental treatment. Presently, medical AI has achieved significant advances, and big data quality management, new technology empowerment innovation, multi-domain knowledge integration, and personalized medical decision-making will exhibit greater growth potential in the clinical arena

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

Colorectal Cancer

Colorectal cancer is one of the commonest cancers affecting individuals across the world. An improvement in survival has been attributed to multidisciplinary management, better diagnostics, improved surgical options for the primary and metastatic disease and advances in adjuvant therapy. In this book, international experts share their experience and knowledge on these different aspects in the management of colorectal cancer. An in depth analysis of screening for colorectal cancer, detailed evaluation of diagnostic modalities in staging colorectal cancer, recent advances in adjuvant therapy and principles and trends in the surgical management of colorectal cancer is provided. This will certainly prove to be an interesting and informative read for any clinician involved in the management of patients with colorectal cancer