Segmentation of Skin Lesions Using Level Set Method

Diagnosis of skin cancers with dermoscopy has been widely accepted as a clinical routine. However, the diagnostic accuracy using dermoscopy relies on the subjective judgment of the dermatologist. To solve this problem, a computer-aided diagnosis system is demanded. Here, we propose a level set method to fulfill the segmentation of skin lesions presented in dermoscopic images. The differences between normal skin and skin lesions in the color channels are combined to define the speed function, with which the evolving curve can be guided to reach the boundary of skin lesions. The proposed algorithm is robust against the influences of noise, hair, and skin textures, and provides a flexible way for segmentation. Numerical experiments demonstrated the effectiveness of the novel algorithm

Novel Computerised Techniques for Recognition and Analysis of Diabetic Foot Ulcers

Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of Diabetes Mellitus (DM). It has been estimated that patients with diabetes have a lifetime risk of 15% to 25% in developing DFU contributing up to 85% of the lower limb amputation due to failure to recognise and treat DFU properly. Current practice for DFU screening involves manual inspection of the foot by podiatrists and further medical tests such as vascular and blood tests are used to determine the presence of ischemia and infection in DFU. A comprehensive review of computerized techniques for recognition of DFU has been performed to identify the work done so far in this field. During this stage, it became clear that computerized analysis of DFU is relatively emerging field that is why related literature and research works are limited. There is also a lack of standardised public database of DFU and other wound-related pathologies. We have received approximately 1500 DFU images through the ethical approval with Lancashire Teaching Hospitals. In this work, we standardised both DFU dataset and expert annotations to perform different computer vision tasks such as classification, segmentation and localization on popular deep learning frameworks. The main focus of this thesis is to develop automatic computer vision methods that can recognise the DFU of different stages and grades. Firstly, we used machine learning algorithms to classify the DFU patches against normal skin patches of the foot region to determine the possible misclassified cases of both classes. Secondly, we used fully convolutional networks for the segmentation of DFU and surrounding skin in full foot images with high specificity and sensitivity. Finally, we used robust and lightweight deep localisation methods in mobile devices to detect the DFU on foot images for remote monitoring. Despite receiving very good performance for the recognition of DFU, these algorithms were not able to detect pre-ulcer conditions and very subtle DFU. Although recognition of DFU by computer vision algorithms is a valuable study, we performed the further analysis of DFU on foot images to determine factors that predict the risk of amputation such as the presence of infection and ischemia in DFU. The complete DFU diagnosis system with these computer vision algorithms have the potential to deliver a paradigm shift in diabetic foot care among diabetic patients, which represent a cost-effective, remote and convenient healthcare solution with more data and expert annotations

New Opportunities and Challenges for Health Professionals in the era of Artificial Intelligence – Review

Introduction and purpose: Modern medical knowledge has grown to a vastness incomprehensible for a single health professional to learn and accommodate. The usage of modern information technologies comes to help, one of them being artificial intelligence, a branch of computer science aimed at developing solutions to perform tasks similar to the human brain, but more efficient and complex, without actual human intervention.  The goal of this review is to provide reader with the knowledge how artificial intelligence is applied in various branches of medicine. Brief description of the state of knowledge: In the fields of infectious diseases, including COVID-19 diagnostics, radiology, dermatology and surgery, works lean toward the statement, which suspect application of AI is beneficial for medical practitioners. Programs help to develop statistical models for virus spreading and the creation of antiviral solutions. The radiological application involves the analysis of images to aid radiologists in diagnosing certain features, similarly to dermatology, where eg. AI can identify malignancy of skin nevi. In the department of surgery, predictive algorithms can help in choosing operation methods and improve outcomes. Conclusions: Usage of AI assistance in the medical field has proven to be successful, but it is yet to be commonly encountered in everyday work. Programs need to be further developed and made more approachable to users without expertise in the IT field. AI may also prove useful in the process of education of health professionals

Current trends of segmentation algorithms for skin lesions

Skin cancer has become one of the most frequent forms of cancer nowadays; its high prevalence has attracted many studies towards the causes and treatments in the recent years. However, the current practice of detecting skin cancers is fairly subjective and may suffer from diagnostic errors. In order to solve this problem, an effective computer-aided diagnosis (CAD) system is urgently demanded. Such system can provide an objective source to help the dermatologist improve the diagnostic accuracy. Such an automated system aims to detect the skin lesions on the acquired images and then analyzes whether those lesions are benign or malignant. The usual computational procedure is composed of three steps: image segmentation, feature extraction, and classification. Among these steps, the segmentation has deterministic influences to the later quantitative analysis and classification; however, due to the complicated appearance of skin lesions in the images, correct segmentation of their boundaries is very challenging. Many algorithms have been proposed to fulfill this task, and some of them have achieved satisfactory performances. Nevertheless, the performance of the existing algorithms still needs further improvement to be accepted in clinical practice. This paper will review these algorithms and summarize their trends of the development; algorithms focused in this work contain both the ones for dermoscopic images and the ones for macroscopic images. Advantages and disadvantages of each algorithm will be discussed; and possible techniques that can be used for improvement will be proposed. Open image database will be used for testing and for the illustration and comparisons among the different algorithms

Artificial intelligence in digital pathology of cutaneous lymphomas: A review of the current state and future perspectives

Primary cutaneous lymphomas (CLs) represent a heterogeneous group of T-cell lymphomas and B-cell lymphomas that present in the skin without evidence of extracutaneous involvement at time of diagnosis. CLs are largely distinct from their systemic counterparts in clinical presentation, histopathology, and biological behavior and, therefore, require different therapeutic management. Additional diagnostic burden is added by the fact that several benign inflammatory dermatoses mimic CL subtypes, requiring clinicopathological correlation for definitive diagnosis. Due to the heterogeneity and rarity of CL, adjunct diagnostic tools are welcomed, especially by pathologists without expertise in this field or with limited access to a centralized specialist panel. The transition into digital pathology workflows enables artificial intelligence (AI)-based analysis of patients’ whole-slide pathology images (WSIs). AI can be used to automate manual processes in histopathology but, more importantly, can be applied to complex diagnostic tasks, especially suitable for rare disease like CL. To date, AI-based applications for CL have been minimally explored in literature. However, in other skin cancers and systemic lymphomas, disciplines that are recognized here as the building blocks for CLs, several studies demonstrated promising results using AI for disease diagnosis and subclassification, cancer detection, specimen triaging, and outcome prediction. Additionally, AI allows discovery of novel biomarkers or may help to quantify established biomarkers. This review summarizes and blends applications of AI in pathology of skin cancer and lymphoma and proposes how these findings can be applied to diagnostics of CL

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of single vs multimodality screening of breast cancer diagnosis with 31 clinicians and 566 images, and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI). We summarize our work with recommendations from the radiologists for guiding the future design of medical imaging interfaces.Comment: AVI 2020 Short Papers, 5 pages, 2 figures, for associated files, see https://github.com/MIMBCD-UI/avi-2020-short-pape

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally

Computer-Aided Diagnosis for Melanoma using Ontology and Deep Learning Approaches

The emergence of deep-learning algorithms provides great potential to enhance the prediction performance of computer-aided supporting diagnosis systems. Recent research efforts indicated that well-trained algorithms could achieve the accuracy level of experienced senior clinicians in the Dermatology field. However, the lack of interpretability and transparency hinders the algorithms’ utility in real-life. Physicians and patients require a certain level of interpretability for them to accept and trust the results. Another limitation of AI algorithms is the lack of consideration of other information related to the disease diagnosis, for example some typical dermoscopic features and diagnostic guidelines. Clinical guidelines for skin disease diagnosis are designed based on dermoscopic features. However, a structured and standard representation of the relevant knowledge in the skin disease domain is lacking. To address the above challenges, this dissertation builds an ontology capable of formally representing the knowledge of dermoscopic features and develops an explainable deep learning model able to diagnose skin diseases and dermoscopic features. Additionally, large-scale, unlabeled datasets can learn from the trained model and automate the feature generation process. The computer vision aided feature extraction algorithms are combined with the deep learning model to improve the overall classification accuracy and save manual annotation efforts