SignificanceMalignant skin tumors, which include melanoma and nonmelanoma skin cancers, are the most prevalent type of malignant tumor. Gross pathology of pigmented skin lesions (PSL) remains manual, time-consuming, and heavily dependent on the expertise of the medical personnel. Hyperspectral imaging (HSI) can assist in the detection of tumors and evaluate the status of tumor margins by their spectral signatures.AimTumor segmentation of medical HSI data is a research field. The goal of this study is to propose a framework for HSI-based tumor segmentation of PSL.ApproachAn HSI dataset of 28 PSL was prepared. Two frameworks for data preprocessing and tumor segmentation were proposed. Models based on machine learning and deep learning were used at the core of each framework.ResultsCross-validation performance showed that pixel-wise processing achieves higher segmentation performance, in terms of the Jaccard coefficient. Simultaneous use of spatio-spectral features produced more comprehensive tumor masks. A three-dimensional Xception-based network achieved performance similar to state-of-the-art networks while allowing for more detailed detection of the tumor border.ConclusionsGood performance was achieved for melanocytic lesions, but margins were difficult to detect in some cases of basal cell carcinoma. The frameworks proposed in this study could be further improved for robustness against different pathologies and detailed delineation of tissue margins to facilitate computer-assisted diagnosis during gross pathology.
Evaluation of tissue margins and hemodynamics is necessary during macropathology of skin lesions. This study aims to produce saliency maps of skin chromophores from ex-vivo specimens and observe the effect of formalin fixation on the maps. We used a multi-spectral imaging system with narrow-band illumination to capture various skin lesions. Saliency maps were produced with three different methods adapted from the literature by utilizing spectral absorption and absorption slope. Saliency maps derived from fixed and unfixed tissue were registered and subsequently compared in terms of correlation and histogram similarity. Preliminary results show high dissimilarity between maps of fixed and unfixed tissue, highlighting the influence of formalin fixing on hemodynamics, while relative distribution of melanin remained mostly unaffected.
Cytology, a method of estimating cancer or cellular atypia from microscopic images of scraped specimens, is used according to the pathologist’s experience to diagnose cases based on the degree of structural changes and atypia. Several methods of cell feature quantification, including nuclear size, nuclear shape, cytoplasm size, and chromatin texture, have been studied. We focus on chromatin distribution in the cell nucleus and propose new feature values that indicate the chromatin complexity, spreading, and bias, including convex hull ratio on multiple binary images, intensity distribution from the gravity center, and tangential component intensity and texture biases. The characteristics and cellular classification accuracies of the proposed features were verified through experiments using cervical smear samples, for which clear nuclear morphologic diagnostic criteria are available. In this experiment, we also used a stepwise support vector machine to create a machine learning model and a cross-validation algorithm with which to derive identification accuracy. Our results demonstrate the effectiveness of our proposed feature values.
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