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. 2017 Dec 20;7:17946. doi: 10.1038/s41598-017-17858-1

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Single-cell nuclear mechanical diagnostics (SCENMED) platform for cancer prognosis. (a) Analysis pipeline to identify nuclei and extract nuclear morphometric features. Cells are first fixed and imaged. The images are then classified using a linear model based on the extracted nuclear morphometric features and a convolutional neural network based on the segmented images. (b) Representative images of nuclei for NIH/3T3, BJ, HMEC, MCF10A, MCF7, and MDA-MB-231. (c) Adaptation of the VGG Convolutional Neural Network architecture consisting of an initial batch normalization layer followed by 10 layers of convolutions, 4 max pooling layers (kernel size 2, stride size 2), 2 fully connected layers, and a softmax output layer. Each convolution is followed by PReLU activations, and the fully connected layers are followed by ReLU activations.