Robust Automatic Rodent Brain Extraction Using 3-D Pulse-Coupled Neural Networks (PCNN)

• Published in: IEEE transactions on image processing Vol. 20; no. 9; pp. 2554 - 2564
• Main Authors: Chou, Nigel, Jiarong Wu, Bai Bingren, Jordan, Anqi Qiu, Kai-Hsiang Chuang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Applied sciences; Artificial intelligence; Automation; Biological and medical sciences; Brain; Brain - anatomy & histology; Brain models; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Connectionism. Neural networks; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Extraction; Human; Image processing; Image Processing, Computer-Assisted - methods; Indexes; Information, signal and communications theory; Magnetic Resonance Imaging - methods; Male; Medical management aid. Diagnosis aid; Medical sciences; Methods; Mice; Mice, Inbred C57BL; MRI; Neural networks; Neural Networks (Computer); Preprocessing; pulse-coupled neural networks (PCNNs); rodent brain; Rodents; segmentation; Signal and communications theory; Signal processing; Signal to noise ratio; Signal, noise; skull-stripping; Solid modeling; Telecommunications and information theory; Three dimensional displays; Performance evaluation; Segmentation; rodent brain; MRI; Neural network; Algorithm; Nuclear magnetic resonance imaging; skull-stripping; Automatic processing; pulse-coupled neural networks (PCNNs); Perceptron; Volume; Imaging; Test method; Signal processing; Feature extraction; Medical imagery; Automatic recognition; Signal to noise ratio
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2011.2126587

Brain extraction is an important preprocessing step for further processing (e.g., registration and morphometric analysis) of brain MRI data. Due to the operator-dependent and time-consuming nature of manual extraction, automated or semi-automated methods are essential for large-scale studies. Automatic methods are widely available for human brain imaging, but they are not optimized for rodent brains and hence may not perform well. To date, little work has been done on rodent brain extraction. We present an extended pulse-coupled neural network algorithm that operates in 3-D on the entire image volume. We evaluated its performance under varying SNR and resolution and tested this method against the brain-surface extractor (BSE) and a level-set algorithm proposed for mouse brain. The results show that this method outperforms existing methods and is robust under low SNR and with partial volume effects at lower resolutions. Together with the advantage of minimal user intervention, this method will facilitate automatic processing of large-scale rodent brain studies.