Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm

Xu, Hongming; Mandal, Mrinal

doi:10.1186/s13640-015-0076-3

Cited by 35 publications

(22 citation statements)

References 31 publications

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“…The evaluation of two-class distinction is performed by using recall (Rec), specificity (Spe) and accuracy (Acc) [13]. In addition, we also compute the area under the receiver operating characteristic (ROC) curve to evaluate two-class distinction performance, which is denoted by Auc.…”

Section: Resultsmentioning

confidence: 99%

Statistical Local Binary Patterns (Slbp): Application to Prostate Cancer Gleason Score Prediction From Whole Slide Pathology Images

Hwang

2018

Preprint

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Computerized whole slide image analysis is important for assisting pathologists in cancer grading and predicting patient clinical outcomes. However, it is challenging to analyze whole slide image (WSI) at cellular level due to its huge size and nuclear variations. For efficient WSI analysis, this paper presents a general texture descriptor, statistical local binary patterns (SLBP), which is applied to prostate cancer Gleason score prediction from WSI. Unlike traditional local binary patterns (LBP) and many its variants, the presented SLBP encodes local texture patterns via analyzing both median and standard deviation over a regional sampling scheme, so that it can capture more micro-and macro-structure information in the image. Experiments on Gleason score prediction have been performed on 317 different patient cases selected from the cancer genome atlas (TCGA) dataset. The presented SLBP descriptor provides over 80% accuracy on two-class (grade ≤7 vs grade ≥8) distinction, which is superior to traditional texture descriptors such as histogram, Haralick and other state-of-art LBP variants.Index Terms-pathology image analysis, prostate cancer grading, local binary patterns

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Section: Resultsmentioning

confidence: 99%

Statistical Local Binary Patterns (Slbp): Application to Prostate Cancer Gleason Score Prediction From Whole Slide Pathology Images

Hwang

2018

Preprint

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“…There are only a few works in the literature which cover automatic processing of histopathological whole slide images of skin specimens stained with hematoxylin and eosin (H&E), the standard stain in histopathology. Some notable examples include an automated algorithm for the diagnostics of melanocytic tumors by Xu et al [40] (based on the melanocyte detection technique described in [41] and the epidermis segmentation approach described in [42]), a method capable of differentiating squamous cell carcinoma in situ from actinic keratosis by Noroozi and Zakerolhosseini [43] and a method for classifying histopathological skin images of three common skin lesions: basal cell carcinomas, dermal nevi, and seborrheic keratoses by Olsen et al [44]. The first two methods are based on classic algorithms for image processing and machine learning, whereas the last one uses deep neural networks.…”

Section: Related Workmentioning

confidence: 99%

Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders

Kucharski

Kłeczek

Jaworek-Korjakowska

et al. 2020

Sensors

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In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions. However, to the best of our knowledge, it is the first described method to segment the nests region. The novelty of our approach is not only the area of research, but, furthermore, we address a problem with a small ground-truth dataset. We propose an effective computer-vision based deep learning tool that can perform the nests segmentation based on an autoencoder architecture with two learning steps. Experimental results verified the effectiveness of the proposed approach and its ability to segment nests areas with Dice similarity coefficient 0.81, sensitivity 0.76, and specificity 0.94, which is a state-of-the-art result.

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“…1), and binarize gray-scale WSI by an empirically selected threshold τ (τ = 210), which generates a binary mask I b with bladder tissue regions (with relatively dark color) as the foreground. Morphological operations [21] are then applied to remove noisy regions and fill small holes within the foreground. In Fig.…”

Section: Input Pathology Slidementioning

confidence: 99%

Spatial heterogeneity and organization of tumor mutation burden and immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

Park

Clemenceau

et al. 2019

Preprint

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The tumor mutational burden (TMB) is a genomic biomarker, which can help in identifying patients most likely to benefit from immunotherapy across a wide range of tumor types including bladder cancer. DNA sequencing, such as whole exome sequencing (WES) is typically used to determine the number of acquired mutations in the tumor. However, WES is expensive, time consuming and not applicable to all patients, and hence it is difficult to be incorporated into clinical practice. This study investigates the feasibility to predict bladder cancer patients TMB by using histological image features. We design an automated whole slide image analysis pipeline that predicts bladder cancer patient TMB via histological features extracted by using transfer learning on deep convolutional networks. The designed pipeline is evaluated to publicly available large histopathology image dataset for a cohort of 253 patients with bladder cancer obtained from The Cancer Genome Atlas (TCGA) project. Experimental results show that our technique provides over 73% classification accuracy, and an area under the receiver operating characteristic curve of 0.75 in distinguishing low and high TMB patients. In addition, it is found that the predicted low and high TMB patients have statistically different survivals, with the p value of 0.047. Our results suggest that bladder cancer patient TMB is predictable by using histological image features derived from digitized H&E slides. Our method is extensible to histopathology images of other organs for predicting patient clinical outcomes.

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Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm

Cited by 35 publications

References 31 publications

Statistical Local Binary Patterns (Slbp): Application to Prostate Cancer Gleason Score Prediction From Whole Slide Pathology Images

Statistical Local Binary Patterns (Slbp): Application to Prostate Cancer Gleason Score Prediction From Whole Slide Pathology Images

Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders

Spatial heterogeneity and organization of tumor mutation burden and immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

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