Computer aided measurement of melanoma depth of invasion in microscopic images

Mokhtari, Mojgan; Rezaeian, Mahdieh; Gharibzadeh, Shahriar; Malekian, Vahid

doi:10.1016/j.micron.2014.02.001

Cited by 19 publications

(25 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the major research directions in histopathological image analysis is to develop image features for different problems and image types. Commonly used image features include low-level features (color [9,10,15,16,18,21,[27][28][29][30][31], texture [10-14, 18, 28]), object level features (shape [32][33][34][35][36][37], topology [8,11,14,18,26,31]), and semantic features (statistics [19,26], histograms [28,32], bag-of-words [28]). …”

Section: Introductionmentioning

confidence: 99%

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

et al. 2016

View full text Add to dashboard Cite

Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-ofwords model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.

show abstract

Section: Introductionmentioning

confidence: 99%

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

et al. 2016

View full text Add to dashboard Cite

show abstract

“…However, the early diagnosis of skin cancers such as cutaneous melanoma is not trivial, as the malignant melanoma and benign tumors may have similar appearance in their early stages. Although many techniques have been developed for melanoma diagnosis, e.g., epiluminescence microscopy [1] and confocal microscopy [3], which can provide initial diagnosis, the histopathological examination of a whole slide image (WSI) by pathologists remains the gold standard for the diagnosis [4] as the histopathology slides provide a cellular level view of the disease [5].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, epidermis segmentation helps in identifying the relative positions between carcinoma cells and epidermis boundaries. The invasion depth of carcinoma cells into the skin tissue can be measured, which is a critical indicator for skin caner grading and therapy [3].…”

Section: Introductionmentioning

confidence: 99%

“…Both GTSA and CET techniques assume that there are small numbers of cell nuclei present in the dermis area, and they eliminate false positive regions by shape and area analysis. Mokhtari et al [3] developed a system for measuring melanoma depth of invasion in microscopic images, which includes the segmentation of epidermal layer. The epidermal layer is segmented by a morphological closing and global thresholding-based technique (henceforth referred to as the MCGT technique).…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2a shows a skin WSI with manually labeled epidermis contours in our database. Figure 2b-d shows the segmentation results by the GTSA [22], CET [23], and MCGT [3] techniques, respectively. It is observed in Fig.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mandal

2015

J Image Video Proc.

View full text Add to dashboard Cite

Automatic segmentation of the epidermis area in skin histopathological images is an essential step for computer-aided diagnosis of various skin cancers. This paper presents a robust technique for epidermis segmentation in the whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then measured by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is assumed to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques.

show abstract

Deep learning-based histopathological image analysis for automated detection and staging of melanoma

Alheejawi

Mandal

et al. 2020

Deep Learning Techniques for Biomedical and Health Informatics

View full text Add to dashboard Cite

Computer aided measurement of melanoma depth of invasion in microscopic images

Cited by 19 publications

References 12 publications

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

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

Deep learning-based histopathological image analysis for automated detection and staging of melanoma

Contact Info

Product

Resources

About