A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages

Artificial Intelligence in Surgery, Surgical Subspecialties, and Related Disciplines

Artificial intelligence (AI) and machine learning (ML) algorithms show promise in revolutionizing many aspects of surgical care. ML algorithms may be used to improve radiologic diagnosis of disease and predict peri-, intra-, and postoperative complications in patients based on their vital signs and other clinical characteristics. Computer vision may improve laparoscopic and minimally invasive surgical education by identifying and tracking the surgeon’s movements and providing real-time performance feedback. Eventually, AI and ML may be used to perform operative interventions that were not previously possible (nanosurgery or endoluminal surgery) with the utilization of fully autonomous surgical robots. Overall, AI will impact every surgical subspecialty, and surgeons must be prepared to facilitate the use of this technology to optimize patient care. This chapter will review the applications of AI across different surgical disciplines, the risks and limitations associated with AI and ML, and the role surgeons will play in implementing this technology into their practice

Development and optimization of near-infrared functional lymphatic imaging in health and lymphedema

The lymphatic vasculature is present in nearly all tissues of the body and serves three primary functions: (1) regulation of tissue fluid homeostasis through the transport of large proteins and excess interstitial fluid, (2) immune cell trafficking, and (3) lipid transport. When the normal function of the lymphatic system deteriorates, many complications can arise. Loss of lymphatic pump function often leads to tissue fluid accumulation, fibrosis, and lipid deposition – a disease known as lymphedema. Despite the critical roles that it performs, very little is known about the lymphatic vasculature in comparison to the blood vasculature. One of the main reasons for this knowledge gap may be the lack of in vivo imaging techniques to non-invasively visualize and obtain quantifiable information regarding lymphatic function, both in health and disease. New techniques are needed to better study lymphatic biology, elucidate the functional role of lymphatics and lymphangiogenesis in health and disease conditions, and better diagnose patients with lymphatic disease at an early stage before any resulting tissue damage is permanent. Near-infrared (NIR) lymphatic imaging has emerged as a new technology for imaging of lymphatic architecture and quantification of vessel function. Although the technique has shown very exciting early results, the technique remains immature and several enhancements specifically for lymphatic imaging and functional quantification remain necessary. Therefore, we have characterized and optimized NIR imaging specifically for lymphatic vessels through a physical and physiological approach. Furthermore, the enhanced NIR lymphatic imaging technique was performed in the context of a novel rodent model of lymphedema to evaluate and characterize the role of lymphatic vessel function in the progression of the disease.Ph.D

Thoracic radiotherapy treatment planning with cine PET/CT

Purpose: Respiratory motion causes substantial uncertainty in radiotherapy treatment planning. Four-dimensional computed tomography (4D-CT) is a useful tool to image tumor motion during normal respiration. Treatment margins can be reduced by targeting the motion path of the tumor. The expense and complexity of 4D-CT, however, may be cost-prohibitive at some facilities. We developed an image processing technique to produce images from cine CT that contain significant motion information without 4D-CT. The purpose of this work was to compare cine CT and 4D-CT for the purposes of target delineation and dose calculation, and to explore the role of PET in target delineation of lung cancer. Methods: To determine whether cine CT could substitute 4D-CT for small mobile lung tumors, we compared target volumes delineated by a physician on cine CT and 4D-CT for 27 tumors with intrafractional motion greater than 1 cm. We assessed dose calculation by comparing dose distributions calculated on respiratory-averaged cine CT and respiratory-averaged 4D-CT using the gamma index. A threshold-based PET segmentation model of size, motion, and source-to-background was developed from phantom scans and validated with 24 lung tumors. Finally, feasibility of integrating cine CT and PET for contouring was assessed on a small group of larger tumors. Results: Cine CT to 4D-CT target volume ratios were (1.05±0.14) and (0.97±0.13) for high-contrast and low-contrast tumors respectively which was within intraobserver variation. Dose distributions on cine CT produced good agreement (\u3c 2%/1 mm) with 4D-CT for 71 of 73 patients. The segmentation model fit the phantom data with R2 = 0.96 and produced PET target volumes that matched CT better than 6 published methods (-5.15%). Application of the model to more complex tumors produced mixed results and further research is necessary to adequately integrate PET and cine CT for delineation. Conclusions: Cine CT can be used for target delineation of small mobile lesions with minimal differences to 4D-CT. PET, utilizing the segmentation model, can provide additional contrast. Additional research is required to assess the efficacy of complex tumor delineation with cine CT and PET. Respiratory-averaged cine CT can substitute respiratory-averaged 4D-CT for dose calculation with negligible differences

Deep learning for identifying Lung Diseases

Growing health problems, such as lung diseases, especially for children and the elderly, require better diagnostic methods, such as computer-based solutions, and it is crucial to detect and treat these problems early. The purpose of this article is to design and implement a new computer vision-based algorithm based on lung disease diagnosis, which has better performance in lung disease recognition than previous models to reduce lung-related health problems and costs . In addition, we have improved the accuracy of the five lung diseases detection, which helps doctors and doctors use computers to solve this problem at an early stage

Translational Research of Audiovisual Biofeedback: An investigation of respiratory-guidance in lung and liver cancer patient radiation therapy

Through the act of breathing, thoracic and abdominal anatomy is in constant motion and is typically irregular. This irregular motion can exacerbate errors in radiation therapy, breathing guidance interventions operate to minimise these errors. However, much of the breathing guidance investigations have not directly quantified the impact of regular breathing on radiation therapy accuracy. The first aim of this thesis was to critically appraise the literature in terms of the use of breathing guidance interventions via systematic review. This review found that 21 of the 27 identified studies yielded significant improvements from the use of breathing guidance. None of the studies were randomised and no studies quantified the impact on 4DCT image quality. The second aim of this thesis was to quantify the impact of audiovisual biofeedback breathing guidance on 4DCT. This study utilised data from an MRI study to program the motion of a digital phantom prior to then simulating 4DCT imaging. Audiovisual biofeedback demonstrated to significantly improved 4DCT image quality over free breathing. The third aim of this thesis was to assess the impact of audiovisual biofeedback on liver cancer patient breathing over a course of stereotactic body radiation therapy (SBRT). The findings of this study demonstrated the effectiveness of audiovisual biofeedback in producing consistent interfraction respiratory motion over a course of SBRT. The fourth aim of this thesis was to design and implement a phase II clinical trial investigating the use and impact of audiovisual biofeedback in lung cancer radiation therapy. The findings of a retrospective analysis were utilised to design and determine the statistics of the most comprehensive breathing guidance study to date: a randomised, stratified, multi-site, phase II clinical trial.. The fifth aim of this thesis was to explore the next stages of audiovisual biofeedback in terms of translating evidence into broader clinical use through commercialisation. This aim was achieved by investigating the the product-market fit of the audiovisual biofeedback technology. The culmination of these findings demonstrates the clinical benefit of the audiovisual biofeedback respiratory guidance system and the possibility to make breathing guidance systems more widely available to patients