Photoacoustic imaging of colorectal cancer and ovarian cancer

Photoacoustic (PA) imaging is an emerging hybrid imaging technology that uses a short-pulsed laser to excite tissue. The resulting photoacoustic waves are used to image the optical absorption distribution of the tissue, which is directly related to micro-vessel networks and thus to tumor angiogenesis, a key process in tumor growth and metastasis. In this thesis, the acoustic-resolution photoacoustic microscopy (AR-PAM) was first investigated on its role in human colorectal tissue imaging, and the optical-resolution photoacoustic microscopy (OR-PAM) was investigated on its role in human ovarian tissue imaging.Colorectal cancer is the second leading cause of cancer death in the United States. Significant limitations in screening and surveillance modalities continue to hamper early detection of primary cancers or recurrences after therapy. In the first phase of the study, benchtop co-registered ultrasound (US) and AR-PAM systems were constructed and tested in ex vivo human colorectal tissue. In the second phase of the study, a co-registered endorectal AR-PAM imaging system was constructed, and a pilot patient study was conducted on patients with rectal cancer treated with radiation and chemotherapy. To automate the data analysis, we designed and trained convolutional neural networks (PAM-CNN and US-CNN) using mixed ex vivo and in vivo patient data. 22 patients’ ex vivo specimens and five patients’ in vivo images (a total of 2693 US ROIs and 2208 PA ROIs) were used for CNN training and validation. Data from five additional patients were used for testing. A total of 32 participants (mean age, 60 years, range, 35-89 years) were evaluated. Unique PAM imaging markers of complete tumor response were found, specifically recovery of normal submucosal vascular architecture within the treated tumor bed. The PAM-CNN model captured this recovery process and correctly differentiated these changes from a residual tumor tissue. The imaging system remained highly capable of differentiating tumor from normal tissue, achieving an area under receiver operating characteristic curve (AUC) of 0.98 from the five patients tested. By comparison, US-CNN had an AUC of 0.71. As an alternative to CNN, a generalized linear model (GLM) was investigated for classification and results showed that CNN outperformed GLM in classification of both US and PAM images. Ovarian cancer is the leading cause of death among gynecological cancers but is poorly amenable to preoperative diagnosis. In the second project of this thesis, we have investigated the feasibility of “optical biopsy,” using OR-PAM to quantify the microvasculature of ovarian tissue and fallopian tube tissue. The technique was demonstrated using excised human ovary and fallopian tube specimens imaged immediately after surgery. Initially, a commercial software Amira was used to characterize tissue vasculature patterns, and later, an effective and easy-access algorithm was developed to quantify the mean diameter, total length, total volume, and fulfillment rate of tissue vasculature. Our initial results demonstrate the potential of OR-PAM as an imaging tool for quick assessment of ovarian tissue and fallopian tube tissue

Focal Spot, Winter 2008/2009

https://digitalcommons.wustl.edu/focal_spot_archives/1110/thumbnail.jp

Framework for the detection and classification of colorectal polyps

In this thesis we propose a framework for the detection and classification of colorectal polyps to assist endoscopists in bowel cancer screening. Such a system will help reduce not only the miss rate of possibly malignant polyps during screening but also reduce the number of unnecessary polypectomies where the histopathologic analysis could be spared. Our polyp detection scheme is based on a cascade filter to pre-process the incoming video frames, select a group of candidate polyp regions and then proceed to algorithmically isolate the most probable polyps based on their geometry. We also tested this system on a number of endoscopic and capsule endoscopy videos collected with the help of our clinical collaborators. Furthermore, we developed and tested a classification system for distinguishing cancerous colorectal polyps from non-cancerous ones. By analyzing the surface vasculature of high magnification polyp images from two endoscopic platforms we extracted a number of features based primarily on the vessel contrast, orientation and colour. The feature space was then filtered as to leave only the most relevant subset and this was subsequently used to train our classifier. In addition, we examined the scenario of splitting up the polyp surface into patches and including only the most feature rich areas into our classifier instead of the surface as a whole. The stability of our feature space relative to patch size was also examined to ensure reliable and robust classification. In addition, we devised a scale selection strategy to minimize the effect of inconsistencies in magnification and geometric polyp size between samples. Lastly, several techniques were also employed to ensure that our results will generalise well in real world practise. We believe this to be a solid step in forming a toolbox designed to aid endoscopists not only in the detection but also in the optical biopsy of colorectal polyps during in vivo colonoscopy.Open Acces

Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy

The detection and assessment of intravascular filling defects is important, because they may represent a process central to cerebral malaria pathogenesis: neurovascular sequestration. We have developed and validated a framework that can automatically detect intravascular filling defects in fluorescein angiogram images. It first employs a state-of-the-art segmentation approach to extract the vessels from images and then divide them into individual segments by geometrical analysis. A feature vector based on the intensity and shape of saliency maps is generated to represent the level of abnormality of each vessel segment. An AdaBoost classifier with weighted cost coefficient is trained to classify the vessel segments into normal and abnormal categories. To demonstrate its effectiveness, we apply this framework to 6,358 vessel segments in images from 10 patients with malarial retinopathy. The test sensitivity, specificity, accuracy, and area under curve (AUC) are 74.7%, 73.5%, 74.1% and 74.2% respectively when compared to the reference standard of human expert manual annotations. This performance is comparable to the agreement that we find between human observers of intravascular filling defects. Our method will be a powerful new tool for studying malarial retinopathy

An overview of the clinical applications of optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) has emerged as a novel, non-invasive imaging modality that allows the detailed study of flow within the vascular structures of the eye. Compared to conventional dye angiography, OCTA can produce more detailed, higher resolution images of the vasculature without the added risk of dye injection. In our review, we discuss the advantages and disadvantages of this new technology in comparison to conventional dye angiography. We provide an overview of the current OCTA technology available, compare the various commercial OCTA machines technical specifications and discuss some future software improvements. An approach to the interpretation of OCTA images by correlating images to other multimodal imaging with attention to identifying potential artefacts will be outlined and may be useful to ophthalmologists, particularly those who are currently still unfamiliar with this new technology. This review is based on a search of peer-reviewed published papers relevant to OCTA according to our current knowledge, up to January 2017, available on the PubMed database. Currently, many of the published studies have focused on OCTA imaging of the retina, in particular, the use of OCTA in the diagnosis and management of common retinal diseases such as age-related macular degeneration and retinal vascular diseases. In addition, we describe clinical applications for OCTA imaging in inflammatory diseases, optic nerve diseases and anterior segment diseases. This review is based on both the current literature and the clinical experience of our individual authors, with an emphasis on the clinical applications of this imaging technology.Eye advance online publication, 8 September 2017; doi:10.1038/eye.2017.181

Generаl pаthomorphology

The manual presents the content and basic questions of the topic, practical skills in sufficient volume for each class to be mastered by students, algorithms for describing macro- and micropreparations, situational tasks. The formulation of tests, their number and variable level of difficulty, sufficient volume for each topic allows to recommend them as preparation for students to take the licensed integrated exam "STEP-1"

Enhancing endoscopic navigation and polyp detection using artificial intelligence

Colorectal cancer (CRC) is one most common and deadly forms of cancer. It has a very high mortality rate if the disease advances to late stages however early diagnosis and treatment can be curative is hence essential to enhancing disease management. Colonoscopy is considered the gold standard for CRC screening and early therapeutic treatment. The effectiveness of colonoscopy is highly dependent on the operator’s skill, as a high level of hand-eye coordination is required to control the endoscope and fully examine the colon wall. Because of this, detection rates can vary between different gastroenterologists and technology have been proposed as solutions to assist disease detection and standardise detection rates. This thesis focuses on developing artificial intelligence algorithms to assist gastroenterologists during colonoscopy with the potential to ensure a baseline standard of quality in CRC screening. To achieve such assistance, the technical contributions develop deep learning methods and architectures for automated endoscopic image analysis to address both the detection of lesions in the endoscopic image and the 3D mapping of the endoluminal environment. The proposed detection models can run in real-time and assist visualization of different polyp types. Meanwhile the 3D reconstruction and mapping models developed are the basis for ensuring that the entire colon has been examined appropriately and to support quantitative measurement of polyp sizes using the image during a procedure. Results and validation studies presented within the thesis demonstrate how the developed algorithms perform on both general scenes and on clinical data. The feasibility of clinical translation is demonstrated for all of the models on endoscopic data from human participants during CRC screening examinations

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

Multidimensional image analysis of cardiac function in MRI

Cardiac morphology is a key indicator of cardiac health. Important metrics that are currently in clinical use are left-ventricle cardiac ejection fraction, cardiac muscle (myocardium) mass, myocardium thickness and myocardium thickening over the cardiac cycle. Advances in imaging technologies have led to an increase in temporal and spatial resolution. Such an increase in data presents a laborious task for medical practitioners to analyse. In this thesis, measurement of the cardiac left-ventricle function is achieved by developing novel methods for the automatic segmentation of the left-ventricle blood-pool and the left ventricle myocardium boundaries. A preliminary challenge faced in this task is the removal of noise from Magnetic Resonance Imaging (MRI) data, which is addressed by using advanced data filtering procedures. Two mechanisms for left-ventricle segmentation are employed. Firstly segmentation of the left ventricle blood-pool for the measurement of ejection fraction is undertaken in the signal intensity domain. Utilising the high discrimination between blood and tissue, a novel methodology based on a statistical partitioning method offers success in localising and segmenting the blood pool of the left ventricle. From this initialisation, the estimation of the outer wall (epi-cardium) of the left ventricle can be achieved using gradient information and prior knowledge. Secondly, a more involved method for extracting the myocardium of the leftventricle is developed, that can better perform segmentation in higher dimensions. Spatial information is incorporated in the segmentation by employing a gradient-based boundary evolution. A level-set scheme is implemented and a novel formulation for the extraction of the cardiac muscle is introduced. Two surfaces, representing the inner and the outer boundaries of the left-ventricle, are simultaneously evolved using a coupling function and supervised with a probabilistic model of expertly assisted manual segmentations