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EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images.
Sornapudi S, Hagerty J, Stanley RJ, Stoecker WV, Long R, Antani S, Thoma G, Zuna R, Frazier SR. Sornapudi S, et al. J Pathol Inform. 2020 Mar 30;11:10. doi: 10.4103/jpi.jpi_53_19. eCollection 2020. J Pathol Inform. 2020. PMID: 32477616 Free PMC article.
CONCLUSIONS: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods....
CONCLUSIONS: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods....
Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.
Sornapudi S, Stanley RJ, Stoecker WV, Almubarak H, Long R, Antani S, Thoma G, Zuna R, Frazier SR. Sornapudi S, et al. J Pathol Inform. 2018 Mar 5;9:5. doi: 10.4103/jpi.jpi_74_17. eCollection 2018. J Pathol Inform. 2018. PMID: 29619277 Free PMC article.
CONCLUSIONS: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods....
CONCLUSIONS: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison …
Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images.
Kaur R, LeAnder R, Mishra NK, Hagerty JR, Kasmi R, Stanley RJ, Celebi ME, Stoecker WV. Kaur R, et al. Skin Res Technol. 2017 Aug;23(3):416-428. doi: 10.1111/srt.12352. Epub 2016 Nov 28. Skin Res Technol. 2017. PMID: 27892649
RESULTS: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics. ...
RESULTS: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods us …
Border detection in dermoscopy images using statistical region merging.
Celebi ME, Kingravi HA, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH, Malters JM, Grichnik JM, Marghoob AA, Rabinovitz HS, Menzies SW. Celebi ME, et al. Skin Res Technol. 2008 Aug;14(3):347-53. doi: 10.1111/j.1600-0846.2008.00301.x. Skin Res Technol. 2008. PMID: 19159382 Free PMC article.
The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like …
The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. T …