Software developed by Biomedical Engineering group and referenced in papers
1. Three-stage classification of Alzheimer’s disease in MRI
Paper: “Three-stage classification of Alzheimer’s disease in MRI in clinical records of the Mass General Brigham Health Care System.”
Download Source Code from GitHub
2. 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.” PMID: 30555750
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.
Region_Detection.py:
– 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.
Region_Filter.py:
- 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.
Data_Loader.py:
- 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.
3. Cell Segmentation for Breast Cancer Cells
Paper: "CytoPAN – Portable cellular analyses for rapid point-of-care cancer diagnosis.” PMID: 32759277
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).
CellAnalysis.m:
- 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.
4. R-Script and Data Files for EV Protein Search
Paper: "A high-throughput magneto-electrochemical array for the integrated isolation and profiling of extracellular vesicles from plasma”
The following files are used to search EV protein markers for colorectal cancer diagnosis.
Marker_selection.Rmd:
– R script to load and filter datasets from public databases, and to compare protein expression levels between normal and tumor tissues.
Marker_selection.nb.html:
– HTML output generated by Marker_selection.Rmd.
evpedia.csv
– List of EV proteins retrieved from EVpedia.