Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin.

Background: Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage.Methods: A new algorithm has been developed which combines enhanced colour information, created following a transformation to the L*a*b* colourspace, with general image intensity information. A colour normalisation step is included to enhance the algorithm's robustness to variations in the lighting and staining of the input images. The resulting optimised image is subjected to thresholding and the segmentation is fine-tuned using a combination of morphological processing and object classification rules. The segmentation algorithm was tested on 40 digital images of haematoxylin & eosin (H&E) stained skin biopsies. Accuracy, sensitivity and specificity of the algorithmic procedure were assessed through the comparison of the proposed methodology against manual methods.Results: Experimental results show the proposed fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5%. When a simple user interaction step is included, the specificity increases to 98.0%, the sensitivity to 91.0% and the accuracy to 96.8%. The algorithm segments effectively for different severities of tissue damage.Conclusions: Epidermal segmentation is a crucial first step in a range of applications including melanoma detection and the assessment of histopathological damage in skin. The proposed methodology is able to segment the epidermis with different levels of histological damage. The basic method framework could be applied to segmentation of other epithelial tissues

Collagen bundle morphometry in skin & scar tissue: a novel distance mapping method provides superior measurements compared to Fourier analysis

Histopathological evaluations of fibrotic processes require the characterization of collagen morphology in terms of geometrical features such as bundle orientation thickness and spacing. However, there are currently no reliable and valid techniques of measuring bundle thickness and spacing. Hence, two objective methods quantifying the collagen bundle thickness and spacing were tested for their reliability and validity: Fourier first-order maximum analysis and Distance Mapping, with the latter constituting a newly developed morphometric technique. Histological slides were constructed and imaged from 50 scar and 50 healthy human skin biopsies and subsequently analyzed by two observers to determine the interobserver reliability via the intraclass correlation coefficient. An intraclass correlation coefficient larger than 0.7 is considered as representing good reliability. The interobserver reliability for the Fourier first-order maximum and for the Distance Mapping algorithms, respectively, showed an intraclass correlation coefficient above 0.72 and 0.89. Additionally, we performed an assessment of validity in the form of responsiveness, in particular, demonstrating medium to excellent results via a calculation of the effect size, highlighting that both methods are sensitive enough to measure a treatment effect in clinical practice. In summary, two reliable and valid measurement methods were demonstrated for collagen bundle morphometry for the first time. Due to its superior reliability and more useful measures (bundle thickness and bundle spacing), Distance Mapping emerges as the preferred and more practical method. Nevertheless, in the future, both methods can be used for reliable and valid collagen morphometry of skin and scars, whereas further applications evaluating the quantitative microscopy of other fibrotic processes are anticipated

Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign skin condition. In order to increase the survival rate, one needs to provide the appropriate treatment early on. To this end, one crucial step for specialists is the evaluation of histopathological slides (glass slides), or Whole Slide Images (WSI), of the patients' skin tissue. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists. First, our algorithm accurately segments WSI into regions that are relevant for an accurate diagnosis, achieving a Mean-IoU of 69% and a Matthews Correlation score of 83% on a novel dataset. Additionally, we also show that our model is competitive with the state of the art on a reference dataset. Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than using only the WSI as the input, since it is also based on the segmentation map. Our segmentation model, which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder which was pre-trained on the Imagenet dataset.Comment: Submitted to https://link.springer.com/chapter/10.1007/978-3-030-52791-4_

Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study

Background: Cutaneous malignant melanoma (CMM) accounts for the highest mortality rate among all skin cancers. Traditional histopathologic diagnosis may be limited by the pathologists’ subjectivity. Second-opinion strategies and multidisciplinary consultations are usually performed to overcome this issue. An available solution in the future could be the use of automated solutions based on a computational algorithm that could help the pathologist in everyday practice. The aim of this pilot study was to investigate the potential diagnostic aid of a machine-based algorithm in the histopathologic diagnosis of CMM. Methods: We retrospectively examined excisional biopsies of 50 CMM and 20 benign congenital compound nevi. Hematoxylin and eosin (H&E) stained WSI were reviewed independently by two expert dermatopathologists. A fully automated pipeline for WSI processing to support the estimation and prioritization of the melanoma areas was developed. Results: The spatial distribution of the nuclei in the sample provided a multi-scale overview of the tumor. A global overview of the lesion's silhouette was achieved and, by increasing the magnification, the topological distribution of the nuclei and the most informative areas of interest for the CMM diagnosis were identified and highlighted. These silhouettes allow the histopathologist to discriminate between nevus and CMM with an accuracy of 96% without any extra information. Conclusion: In this study we proposed an easy-to-use model that produces segmentations of CMM silhouettes at fine detail level

VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations.

BACKGROUND: Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections. METHODS: VIPAR (volume information-based histopathological analysis by 3D reconstruction and data extraction), a light-sheet microscopy-based approach for the analysis of tissue biopsies, is based on digital reconstruction and visualization of microscopic image stacks. VIPAR allows semiautomated segmentation of the vasculature and subsequent nonbiased extraction of characteristic vessel shape and connectivity parameters. We applied VIPAR to analyze biopsies from healthy lymphedematous and lymphangiomatous skin. RESULTS: Digital 3D reconstruction provided a directly visually interpretable, comprehensive representation of the lymphatic and blood vessels in the analyzed tissue volumes. The most conspicuous features were disrupted lymphatic vessels in lymphedematous skin and a hyperplasia (4.36-fold lymphatic vessel volume increase) in the lymphangiomatous skin. Both abnormalities were detected by the connectivity analysis based on extracted vessel shape and structure data. The quantitative evaluation of extracted data revealed a significant reduction of lymphatic segment length (51.3% and 54.2%) and straightness (89.2% and 83.7%) for lymphedematous and lymphangiomatous skin, respectively. Blood vessel length was significantly increased in the lymphangiomatous sample (239.3%). CONCLUSION: VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin. Its application is not limited to the vascular systems or skin. FUNDING: Max Planck Society, DFG (SFB 656), and Cells-in-Motion Cluster of Excellence EXC 1003

Developing improved algorithms for detection and analysis of skin cancer

University of Technology Sydney. Faculty of Engineering and Information Technology.Malignant melanoma is one of the deadliest forms of skin cancer and number of cases showed rapid increase in Europe, America, and Australia over the last few decades. Australia has one of the highest rates of skin cancer in the world, at nearly four times the rates in Canada, the US and the UK. Cancer treatment costs constitute more 7.2% of health system costs. However, a recovery rate of around 95% can be achieved if melanoma is detected at an early stage. Early diagnosis is obviously dependent upon accurate assessment by a medical practitioner. The variations of diagnosis are sufficiency large and there is a lack of detail of the test methods. This thesis investigates the methods for automated analysis of skin images to develop improved algorithms and to extend the functionality of the existing methods used in various stages of the automated diagnostic system. This in the long run can provide an alternative basis for researchers to experiment new and existing methodologies for skin cancer detection and diagnosis to help the medical practitioners. The objective is to have a detailed investigation for the requirements of automated skin cancer diagnostic systems, improve and develop relevant segmentation, feature selection and classification methods to deal with complex structures present in both dermoscopic/digital images and histopathological images. During the course of this thesis, several algorithms were developed. These algorithms were used in skin cancer diagnosis studies and some of them can also be applied in wider machine learning areas. The most important contributions of this thesis can be summarized as below: - Developing new segmentation algorithms designed specifically for skin cancer images including digital images of lesions and histopathalogical images with attention to their respective properties. The proposed algorithm uses a two-stage approach. Initially coarse segmentation of lesion area is done based on histogram analysis based orientation sensitive fuzzy C Mean clustering algorithm. The result of stage 1 is used for the initialization of a level set based algorithm developed for detecting finer differentiating details. The proposed algorithms achieved true detection rate of around 93% for external skin lesion images and around 88% for histopathological images. - Developing adaptive differential evolution based feature selection and parameter optimization algorithm. The proposed method is aimed to come up with an efficient approach to provide good accuracy for the skin cancer detection, while taking care of number of features and parameter tuning of feature selection and classification algorithm, as they all play important role in the overall analysis phase. The proposed method was also tested on 10 standard datasets for different kind of cancers and results shows improved performance for all the datasets compared to various state-of the art methods. - Proposing a parallelized knowledge based learning model which can make better use of the differentiating features along with increasing the generalization capability of the classification phase using advised support vector machine. Two classification algorithms were also developed for skin cancer data analysis, which can make use of both labelled and unlabelled data for training. First one is based on semi advised support vector machine. While the second one based on Deep Learning approach. The method of integrating the results of these two methods is also proposed. The experimental analysis showed very promising results for the appropriate diagnosis of melanoma. The classification accuracy achieved with the help of proposed algorithms was around 95% for external skin lesion classification and around 92 % for histopathalogical image analysis. Skin cancer dataset used in this thesis is obtained mainly from Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital. While for comparative analysis and benchmarking of the few algorithms some standard online cancer datasets were also used. Obtained result shows a good performance in segmentation and classification and can form the basis of more advanced computer aided diagnostic systems. While in future, the developed algorithms can also be extended for other kind of image analysis applications

Automated multimodal spectral histopathology for quantitative diagnosis of residual tumour during basal cell carcinoma surgery

Multimodal spectral histopathology (MSH), an optical technique combining tissue auto-fluorescence (AF) imaging and Raman micro-spectroscopy (RMS), was previously proposed for detection of residual basal cell carcinoma (BCC) at the surface of surgically-resected skin tissue. Here we report the development of a fully-automated prototype instrument based on MSH designed to be used in the clinic and operated by a non-specialist spectroscopy user. The algorithms for the AF image processing and Raman spectroscopy classification had been first optimised on a manually-operated laboratory instrument and then validated on the automated prototype using skin samples from independent patients. We present results on a range of skin samples excised during Mohs micrographic surgery, and demonstrate consistent diagnosis obtained in repeat test measurement, in agreement with the reference histopathology diagnosis. We also show that the prototype instrument can be operated by clinical users (a skin surgeon and a core medical trainee, after only 1-8 hours of training) to obtain consistent results in agreement with histopathology. The development of the new automated prototype and demonstration of inter-instrument transferability of the diagnosis models are important steps on the clinical translation path: it allows the testing of the MSH technology in a relevant clinical environment in order to evaluate its performance on a sufficiently large number of patients