Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Robust Landmark-based Stent Tracking in X-ray Fluoroscopy

In clinical procedures of angioplasty (i.e., open clogged coronary arteries), devices such as balloons and stents need to be placed and expanded in arteries under the guidance of X-ray fluoroscopy. Due to the limitation of X-ray dose, the resulting images are often noisy. To check the correct placement of these devices, typically multiple motion-compensated frames are averaged to enhance the view. Therefore, device tracking is a necessary procedure for this purpose. Even though angioplasty devices are designed to have radiopaque markers for the ease of tracking, current methods struggle to deliver satisfactory results due to the small marker size and complex scenes in angioplasty. In this paper, we propose an end-to-end deep learning framework for single stent tracking, which consists of three hierarchical modules: U-Net based landmark detection, ResNet based stent proposal and feature extraction, and graph convolutional neural network (GCN) based stent tracking that temporally aggregates both spatial information and appearance features. The experiments show that our method performs significantly better in detection compared with the state-of-the-art point-based tracking models. In addition, its fast inference speed satisfies clinical requirements.Comment: Accepted by ECCV 202

Deep Learning in Medical Image Analysis

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements

Computer modelling of metabolic adaptions during mitochondrial dysfunction and machine learning to predict novel mitochondrial disease genes

Mitochondria are organelles found in almost every eukaryote and are primarily responsible for generating chemical energy in the form of adenosine triphosphate. This thesis investigates two main causes of mitochondrial dysfunction: mitochondrial toxicity arising from side-effects of drugs; and mitochondrial diseases arising from defects in nuclear-encoded genes. Novel chemical entities being developed as drug leads are screened for cellular toxicity in which mitochondrial dysfunction is a major cause. However, our lack of understanding of the metabolic adaptations to mitochondrial dysfunction limits the accurate screening of mitochondrial dysfunction for pharmaceutical companies, thus preventing potentially useful drugs from being developed. To further our understanding of these adaptations, I analysed a large-scale metabolomics data set of rats administered a known mitochondrial complex III inhibitor. The analyses revealed many perturbed pathways which can be exploited as biomarkers of mild mitochondrial dysfunction, a condition which is currently clinically undetectable during the drug development process. To direct future studies on mitochondrial dysfunction, a multi-organ model of mitochondrial metabolism was generated and used to simulate inhibition of the mitochondrial respiratory complexes. The simulations of complex III inhibition accurately predicted many of the metabolite behaviours identified in the metabolomics analyses and provided theories for their significance. Simulations of the other complexes’ inhibitions identified many unique behaviours which can be used to direct future studies, studies which would greatly improve our understanding of the metabolic adaptations and provide higher confidence biomarkers. Mitochondrial dysfunction is linked to many late onset diseases such as Parkinson’s, and inborn errors of mitochondrial metabolism cause severe neurological and physiological diseases. Patients with suspected mitochondrial disease have their DNA sequenced and analysed. Diagnosis of mitochondrial disease by sequencing requires knowledge of the mitochondrial proteome, which is currently incomplete. A predicted mitochondrial proteome was generated using a support vector machine trained using the abundance of protein localisation data available in the MitoMiner database. The support vector machine identified 442 novel mitochondrional proteins. The current success rate of diagnosing mitochondrial disease using sequencing is currently limited by our inability to filter and prioritise a patient’s DNA variants. Patients which do not have a variant in one of the already known mitochondrial disease genes are usually left with over hundreds of potential disease-causing variants. A probability of being disease-causing for each gene in the mitochondrial proteome was generated using two trained neural networks. The networks were trained on a large amount of different data sources for differentiating mitochondrial disease genes including protein-protein interaction network metrics, gene tissue expression and protein evolution. The predicted probabilities allow for better filtering and prioritisation of a patient’s variants for candidate disease-causing genes to be experimentally verified. The predicted mitochondrial proteome and their predicted disease-causing probabilities are currently used in an NGS analysis pipeline at the MRC Mitochondrial Biology Unit for diagnosing mitochondrial disease patient samples

Intelligent Image Retrieval Techniques: A Survey

AbstractIn the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same content-based searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques