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Proceedings Paper

Localization and classification of cell nuclei in post-neoadjuvant breast cancer surgical specimen using fully convolutional networks
Author(s): Rene Bidart; Mehrdad J. Gangeh; Mohammad Peikari; Sherine Salama; Sharon Nofech-Mozes; Anne L. Martel; Ali Ghodsi
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Paper Abstract

Neoadjuvant therapy (NAT) is an option for locally advanced breast cancer patients to downsize tumour allowing for less extensive surgical operation, better cosmetic outcomes, and lesser post-operative complications. The quality of NAT is assessed by pathologists after examining the tissue sections to reveal the efficacy of treatment, and also associate the outcome with the patient's prognosis. There are many factors involved with assessing the best treatment efficacy, including the amount of residual cancer within tumour bed. Currently, the process of assessing residual tumour burden is qualitative, which may be time-consuming and impaired by inter-observer variability. In this study, an automated method was developed to localize, and subsequently classify nuclei figures into three categories of lymphocyte (L), benign epithelial (BE), and malignant epithelial (ME) figures from post-NAT tissue slides of breast cancer. A fully convolutional network (FCN) was developed to perform both tasks in an efficient way. In order to find the cell nuclei in image patches (localization), the FCN was applied over the entire patch, generating four heatmaps corresponding to the probability of a pixel being the centre of an L, BE, ME, or non-cell nuclei. Non-maximum suppression algorithm was subsequently applied to the generated heatmaps to estimate the nuclei locations. Finally, the highest probability corresponding to each predicted cell nucleus in the heatmaps was used for the classification of the nucleus to one of the three classes (L, BE, or ME). The final classification accuracy on detected nuclei was 94.6%, surpassing previous machine learning methods based on handcrafted features on this dataset.

Paper Details

Date Published: 6 March 2018
PDF: 8 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810O (6 March 2018); doi: 10.1117/12.2292815
Show Author Affiliations
Rene Bidart, Univ. of Waterloo (Canada)
Mehrdad J. Gangeh, Univ. of Toronto (Canada)
Mohammad Peikari, Univ. of Toronto (Canada)
Sherine Salama, Univ. of Toronto (Canada)
Sharon Nofech-Mozes, Univ. of Toronto (Canada)
Anne L. Martel, Univ. of Toronto (Canada)
Sunnybrook Research Institute (Canada)
Ali Ghodsi, Univ. of Waterloo (Canada)


Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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