Towards an Effective Imaging-Based Decision Support System for Skin Cancer

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct ap plications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suf fers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.info:eu-repo/semantics/publishedVersio

(SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS

Melanoma is a very aggressive form of skin cancer whose incidence has constantly grown in the last 50 years. To increase the survival rate, an early diagnosis followed by a prompt excision is crucial and requires an accurate and periodic analysis of the patient's melanocytic lesions. We have developed an hardware and software solution named Mole Mapper to assist the dermatologists during the diagnostic process. The goal is to increase the accuracy of the diagnosis, accelerating the entire process at the same time. This is achieved through an automated analysis of the dermatoscopic images which computes and highlights the proper information to the dermatologist. In this thesis we present the 3 main algorithms that have been implemented into the Mole Mapper: A robust segmentation of the melanocytic lesion, which is the starting point for any other image processing algorithm and which allows the extraction of useful information about the lesion's shape and size. It outperforms the speed and quality of other state-of-the-art methods, with a precision that meets a Senior Dermatologist's standard and an execution time that allows for real-time video processing; A virtual shaving algorithm, which increases the precision and robustness of the other computer vision algorithms and provides the dermatologist with a hair-free image to be used during the evaluation process. It matches the quality of state-of-the-art methods but requires only a fraction of the computational time, allowing for computation on a mobile device in a time-frame compatible with an interactive GUI; A registration algorithm through which to study the evolution of the lesion over time, highlighting any unexpected anomalies and variations. Since a standard approach to this problem has not yet been proposed, we define the scope and constraints of the problem; we analyze the results and issues of standard registration techniques; and finally, we propose an algorithm with a speed compatible with Mole Mapper's constraints and with an accuracy comparable to the registration performed by a human operator

Stem cells and neoplasia a study of acquired melanocytic naevi

Includes abstract.Includes bibliographical references.Melanocytic neoplasia is a multifaceted process involving a complex interplay of genetic and environmental factors. Despite recent advances, the aetiology and pathogenesis of melanocytic neoplasms remains unclear and the anatomical location and state of differentiation of the initiating cell remains to be elucidated. Traditional models propose melanoma arises from an epidermal melanocyte which passes through defined stages of increasing atypia due to the accumulation of mutational events. The newly proposed tumour stem cell hypothesis, however, advocates melanoma may arise from a mutated tissue-resident precursor cell, and not froma terminally differentiated melanocyte. The overall aim of this study was to investigate whether benign naevi contain cells with a stem cell-like phenotype, and to examine the question of whether these might be stem cell precursors of malignant melanoma. Ten formalin-fixed and paraffin embedded human naevus biopsy samples, of five different naevus subtypes, were systematically re-evaluated by direct immunofluorescence first, to understand the lineage of a “naevus” cell, and second, to evaluate whether melanocytic naevi may originate from a precursor cell and not via de-differentiation from an epidermal melanocyte. For phenotypic characterisation, results were highly suggestive of a melanocytic lineage with 85.36% of naevus cells staining positively for the melanocyte specific differentiation antigen, Melan-A, as determined by a semi-quantitative analysis. Yet, these cells showed important morphological variations and were distinct from differentiated epidermal melanocytes. Furthermore, although studies have suggested regional variations in naevi and a possible Schwann cell lineage, there was no evidence in support of a Schwann cell phenotype of naevus cells in this study. Secondly, precursor markers were identified in all naevus subtypes analysed, thereby convincingly demonstrating the presence of precursor cells within naevus tissue. The majority of positively labelled cells localised to the epidermal compartment (72.72%) and this was similar for all three markers analysed: OCT4 (77.22%), NANOG (63.72%) and p75 (57.15%). Interestingly, dysplastic naevi showed a large proportion of OCT4+ cells (5.81%), which was by far the greatest proportion of any precursor marker identified in this study. As dysplastic subtypes are associated with an increased risk of melanoma development, this may imply an increased stem cell component confers this risk. Thirdly, analysis with the proliferation marker Ki-67 revealed the epidermal compartment contained the majority of dividing naevus cells (76.17%), thereby supporting an epidermal origin of naevi. Since the majority of precursor cells identified were within the epidermal compartment, this may suggest precursor cells drive naevus development, in support of the tumour stem cell hypothesis. In addition, the predominance of these precursor cells within the interfollicular epidermis may aid in identifying a potential stem cell niche. However, no precursor cells were noted in the normal intervening interfollicular epidermis or dermis of naevi, or in the epidermis or dermis of normal human skin, as may have been expected. In conclusion, the presence of stem cell markers in naevus tissue supports the hypothesis that at least some naevus cells may arise from stem cells, and not from differentiated melanocytes

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

A Clinical Study of Congenital Melanocytic Naevus.

INTRODUCTION : Naevus is a Latin word which means maternal impression or birth mark. Naevus is a common skin lesion seen in patients of all ages and both sexes.It is usually present at birth or in early childhood. However, some may develop later in adulthood. Hamartoma is a Greek word which means to err. Hamartoma is a tumour like, non neoplastic proliferation of abnormal mixtures of the normal components of a tissue. Previously naevus and hamartoma were considered to be synonymous but now they are regarded as distinct entities as there is no neoplastic proliferation in hamartoma whereas in naevus neoplastic proliferation occurs. Naevi are of cosmetic significance if they are large and located on visible areas of the body.Their importance also lies in associated defects of organ systems and possible neoplastic potential, benign and malignant. There are several types of naevi .Based on the tissue of origin, they are classified as epidermal naevi, melanocytic naevi, dermal and subcutaneous naevi. Melanocytic naevi are benign neoplasms or hamartomas composed of nevomelanocytes. They are broadly classified based on their being derived from epidermal melalanocytes or from dermal melanocytes. Congenital and acquired melanocytic naevi are derived from epidermal melanocytes while naevus of Ota, naevus of Ito and blue naevi are derived from dermal melanocytes. Congenital melanocytic naevi are anomalies in embryogenesis. They could be considered as malformations or hamartomas, made up of nevomelanocytes which lack normal melanocytic differentiation and occur as ‘nests’ in the epidermis and /or dermis. In this study the age and sex distribution of congenital melanocytic naevi, types of congenital melanocytic naevi, localization , their cutaneous or systemic associations if any, complications, skin biopsy, special staining and treatment of congenital melanocytic naevi were studied. AIM OF THE STUDY : 1. To study the presenting age group and sex distribution of congenital melanocytic naevus. 2. To study the different clinical types of congenital melanocytic naevus. 3. To study the localization of congenital melanocytic naevus. 4. To evaluate the associated cutaneous and systemic conditions of congenital melanocytic naevus. 5. To study the complications of congenital melanocytic naevus. CONCLUSION : The sex distribution of congenital melanocytic naevus was more in females than males in this study. * Congenital melanocytic naevus is classified based on the size of the lesion as small, medium, large, and giant naevus.In this study prevalence of small naevi was more ,followed by medium, giant and large naevi in descending order of * Localisation of small naevi were more on the head and neck region, whereas medium naevi were homogenously distributed in the head and neck ,and chest and back region in this study. * In this study, most of the small congenital melanocytic naevi were single in distribution rather than multiple. * Kissing naevus of the eyelid (Congenital divided naevus ) which falls into the group of medium and large naevus was found in 3 cases which shows the time of development of congenital melanocytic naevi. * Giant naevi of bathing trunk distribution and localization over the head and neck region, posterior axis were noted both in children and adults.CT scan of these patients were normal and involvement of central nervous system was not found in any of these cases

From scans to models: Registration of 3D human shapes exploiting texture information

New scanning technologies are increasing the importance of 3D mesh data, and of algorithms that can reliably register meshes obtained from multiple scans. Surface registration is important e.g. for building full 3D models from partial scans, identifying and tracking objects in a 3D scene, creating statistical shape models. Human body registration is particularly important for many applications, ranging from biomedicine and robotics to the production of movies and video games; but obtaining accurate and reliable registrations is challenging, given the articulated, non-rigidly deformable structure of the human body. In this thesis, we tackle the problem of 3D human body registration. We start by analyzing the current state of the art, and find that: a) most registration techniques rely only on geometric information, which is ambiguous on flat surface areas; b) there is a lack of adequate datasets and benchmarks in the field. We address both issues. Our contribution is threefold. First, we present a model-based registration technique for human meshes that combines geometry and surface texture information to provide highly accurate mesh-to-mesh correspondences. Our approach estimates scene lighting and surface albedo, and uses the albedo to construct a high-resolution textured 3D body model that is brought into registration with multi-camera image data using a robust matching term. Second, by leveraging our technique, we present FAUST (Fine Alignment Using Scan Texture), a novel dataset collecting 300 high-resolution scans of 10 people in a wide range of poses. FAUST is the first dataset providing both real scans and automatically computed, reliable ground-truth correspondences between them. Third, we explore possible uses of our approach in dermatology. By combining our registration technique with a melanocytic lesion segmentation algorithm, we propose a system that automatically detects new or evolving lesions over almost the entire body surface, thus helping dermatologists identify potential melanomas. We conclude this thesis investigating the benefits of using texture information to establish frame-to-frame correspondences in dynamic monocular sequences captured with consumer depth cameras. We outline a novel approach to reconstruct realistic body shape and appearance models from dynamic human performances, and show preliminary results on challenging sequences captured with a Kinect