This public repository contains a user-guided tool for semi-automated cerebral microbleed detection and volume segmentation. The algorithm was developed by Dr. Janine Lupo's group in the Department of Radiology and Biomedical Imaging at the University of California San Francisco.
The algorithm initiates with a Fast Radial Symmetry Transform (FRST) to detect all putative CMB candidates (note: the transform has been modified to detect dark lesion on a light background). Each candidate then undergoes region growing, and hand-crafted features (e.g. circularity, size, centroid shift) are used to discriminate true CMBs from false positives. While this removes many false positives, a handful still remain at this stage.
The remaining candidates are then segmented using a local thresholding technique to quantify individual volumes, and further classify CMBs into one of three categories: 1) A CMB belonging to a single slice, 2) A traveling CMB, or otherwise 3) A possible true CMB or hard mimic. These categories will aid the user in classifying the candidate CMBs in the next step, and are of interest with respect to reporting CMB characteristics.
In the final step, the user is visually presented, one-by-one, with a candidate CMB (and a description of the category they fall under), and asked to classify the candidate as a true CMB or false positive using 'y' and 'n' keyboard responses. The user can also use scrolling to move through the image slices and/or zoom by clicking and simultaneously dragging the mouse upward or downward.
Wei Bian, Melanie A. Morrison, Xiaowei Zhu, Sivakami Avadiappan, Yicheng Chen, Seyedmehdi Payabvash, Mihir Shah, Christopher. P. Hess, Janine M. Lupo
We request that you cite the following publications when using our software in your research. Thank you!
Morrison MA, Payabvash S, Chen Y, Avadiappan S, Shah M, Bian W, Zou X, Hess C, Lupo JM. A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: evaluating vascular injury and data labelling for machine learning; 2018, NeuroImage: Clinical, 20: 498-505.
Bian W, Hess CP, Chang SM, et al. Computer-aided detection of radiation-induced cerebral microbleeds on susceptibility-weighted MR images. NeuroImage Clin 2013; 2:282–90.
The algorithm accepts a single, non-projected volumetric T2*-weighted or SWI dataset in NIFTI format (.nii). A file with input parameters called cmb_threshold is also required. This file is included; a detailed description of each parameter can be found in Bian et al. 2013.
Primary outputs include:
a) scaled_[rootName].nii – image scaled to [0,255]
b) FRST_map_masked[rootName].nii – cmb candidates after FRST
c) FRST_Vessel_mask[rootName].nii – mask of vessels
d) cmb[rootName].nii – cmb candidates after region growing
e) nonproj_cmbseg_v5_thresdeg2x5final_usercorrected[rootName].nii – final cmb candidates
f) nonproj_cmbseg_v5_thresdeg2x5denoised[rootName].nii – all cmb candidates automatically removed at denoising stage
g) nonproj_cmbseg_v5_thresdeg2x5false_positives[rootName].nii – all cmb candidates manually removed as false positives by the user
h) result_[rootName].txt – a text file containing all cmb counts and cmb volumes
MATLAB Protected Files
- Download cmb_detection_2018_nifti_protected.zip
- Download Imagine-Legacy-master.zip (NEW JULY 2020)
- Replace the Imagine directory currently in the cmb_detection_2018_nifti_protected directory with Imagine-Legacy-master
- Add cmb_detection_2018_nifti_protected and sub folders to your path
- cd to the test_subect directory or your equivalent subject directory with your swi.nii file (this will also be the output directory)
- run the following:
cmb_detection('input file','path to cmb_threshold parameter file in directory','diagnostics flag','semi-automatic detection flag');
e.g. cmb_detection(‘test_swi.nii’, ‘/yourPath/cmb_detection_2018_nifti_protected’, ‘diagoff’, ‘semion’);
*When diagoff, the script runs faster and does not produce intermediate files for optimization purposes.
*When semion, the user-guided classification is enabled.
Note: Please use MATLAB version(s) R2017+ for full functionality (i.e. slice scroll, window zoom) of the user-guided GUI.
Update 2021: Please use cmbevaluation2.p recently uploaded instead of the version in the .zip folder IF you wish to run SEMIOFF (i.e. fully automated, though output will have false positives)
The algorithm was optimized on a 7T SWI dataset acquired from 10 adult brain tumor patients with radiological evidence of CMBs following radiation therapy. The overall sensitivity is 86.7%. Performance measures will vary with user classification outcomes.
A test set has been included in this repository. This includes 10 SWI datasets with radiotherapy-induced cerebral microbleed. Five were acquired on a 3T GE system (P01-P05), the other 5 were acquired on a 7T GE system (P06-P10).
Padma Priya (2022). CMB_labeler (https://github.com/LupoLab-UCSF/CMB_labeler), GitHub. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
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