Next Article in Journal
Psychophysical Determination of the Relevant Colours That Describe the Colour Palette of Paintings
Previous Article in Journal
Progressive Secret Sharing with Adaptive Priority and Perfect Reconstruction
Article

Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma

1
Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
2
SCiLS, Bruker Daltonik, 28359 Bremen, Germany
3
Dermatopathologie Duisburg Essen, 45329 Essen, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Constantino Carlos Reyes-Aldasoro
Received: 10 March 2021 / Revised: 29 March 2021 / Accepted: 6 April 2021 / Published: 13 April 2021
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist’s clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have a high probability of exhibiting pathological features in whole slide images (WSI). We focus on detecting basal cell carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues’ exact location on 100 WSI. The rest of the data, with ground-truth sectionwise labels, are used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: (a) deep supervision, (b) linear combination of decoder outputs, and obtain some interpretations about what the network’s decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the Test set. View Full-Text
Keywords: digital pathology; dermatopathology; whole slide image; basal cell carcinoma; skin cancer; deep learning; UNet digital pathology; dermatopathology; whole slide image; basal cell carcinoma; skin cancer; deep learning; UNet
Show Figures

Figure 1

MDPI and ACS Style

Le’Clerc Arrastia, J.; Heilenkötter, N.; Otero Baguer, D.; Hauberg-Lotte, L.; Boskamp, T.; Hetzer, S.; Duschner, N.; Schaller, J.; Maass, P. Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma. J. Imaging 2021, 7, 71. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7040071

AMA Style

Le’Clerc Arrastia J, Heilenkötter N, Otero Baguer D, Hauberg-Lotte L, Boskamp T, Hetzer S, Duschner N, Schaller J, Maass P. Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma. Journal of Imaging. 2021; 7(4):71. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7040071

Chicago/Turabian Style

Le’Clerc Arrastia, Jean, Nick Heilenkötter, Daniel Otero Baguer, Lena Hauberg-Lotte, Tobias Boskamp, Sonja Hetzer, Nicole Duschner, Jörg Schaller, and Peter Maass. 2021. "Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma" Journal of Imaging 7, no. 4: 71. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7040071

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop