Software developed by Biomedical Engineering group and referenced in papers

1. Deep Learning Systems For Lymphoma Diagnostics

Paper: “Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning.”

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This code is part of a project to leverage deep learning systems for lymphoma diagnostics. We use the Maximally Stable Extrema Regions (MSER) blob detection algorithm to find regions of interest in a hologram image of cells and use a trained Convolutional Neural Network (CNN) to filter out non-cell regions.
– Detects cells in a hologram using the MSER algorithm and a pre-trained CNN.
- Contains several helper functions for processing multiple images from a single folder, or multiple folders that each contain multiple images.
- Contains the code to construct a CNN using Keras.
- Can load a pre-trained CNN or build an empty architecture and train it.
- Contains helper function to properly format and classify an input region with a pre-trained CNN.
- Contains numerous functions to load labelled training images from a directory into (x,y) training tensors.
- Can pre-process data to make for easier training.
- Designed to return data in a format that the region filter CNN can be trained with.

2. Cell Segmentation for Breast Cancer Cells

Paper: "CytoPAN – Portable cellular analyses for rapid point-of-care cancer diagnosis.”

This Matlab code is part of a project to identify breast cancer cells in bright-field and measure the mean fluorescence intensity of respective cells in fluorescence imaging for breast cancer biomarkers (QUAD, HER2, ERPR). 

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  • Sobel edge detection method was used to determine cellular edges based on high contrast in the bright-field image.
  • Binary gradient mask underwent dilation, hole filling, and smoothing
  • Mask regions correlated with DAPI signals are identified as biological cells.
  • Mean intensities of biomarkers (QUAD, HER2 and ERPR) of individual biological cells are measured.