A novel computational approach for automatic dendrite spines detection in two-photon laser scan microscopy.

BACKGROUND: Recent research has shown that there is a strong correlation between the functional properties of a neuron and its morphologic structure. Current morphologic analyses typically involve a significant component of computer-assisted manual labor, which is very time-consuming and is susceptible to operator bias. The existing semi-automatic approaches largely reduce user efforts. However, some manual interventions, such as setting a global threshold for segmentation, are still needed during image processing. METHODS: We present an automated approach, which can greatly help neurobiologists obtain quantitative morphological information about a neuron and its spines. The automation includes an adaptive thresholding method, which can yield better segment results than the prevalent global thresholding method. It also introduces an efficient backbone extraction method, a SNR based, detached spine component detection method, and an attached spine component detection method based on the estimation of local dendrite morphology. RESULTS: The morphology information obtained both manually and automatically are compared in detail. Using the Kolmogov-Smirnov test, we find a 99.13% probability that the dendrite length distributions are the same for the automatic and manual processing methods. The spine detection results are also compared with other existing semi-automatic approaches. The comparison results show that our approach has 33% fewer false positives and 77% fewer false negatives on average. CONCLUSIONS: Because the proposed detection algorithm requires less user input and performs better than existing algorithms, our approach can quickly and accurately process neuron images without user intervention.

Chemically-selective imaging of brain structures with CARS microscopy.

We demonstrate the use of coherent anti-Stokes Raman scattering (CARS) microscopy to image brain structure and pathology ex vivo. Although non-invasive clinical brain imaging with CT, MRI and PET has transformed the diagnosis of neurologic disease, definitive pre-operative distinction of neoplastic and benign pathologies remains elusive. Definitive diagnosis still requires brain biopsy in a significant number of cases. CARS microscopy, a nonlinear, vibrationally-sensitive technique, is capable of high-sensitivity chemically-selective three-dimensional imaging without exogenous labeling agents. Like MRI, CARS can be tuned to provide a wide variety of possible tissue contrasts, but with sub-cellular spatial resolution and near real time temporal resolution. These attributes make CARS an ideal technique for fast, minimally invasive, non-destructive, molecularly specific intraoperative optical diagnosis of brain lesions. This promises significant clinical benefit to neurosurgical patients by providing definitive diagnosis of neoplasia prior to tissue biopsy or resection. CARS imaging can augment the diagnostic accuracy of traditional frozen section histopathology in needle biopsy and dynamically define the margins of tumor resection during brain surgery. This report illustrates the feasibility of in vivo CARS vibrational histology as a clinical tool for neuropathological diagnosis by demonstrating the use of CARS microscopy in identifying normal brain structures and primary glioma in fresh unfixed and unstained ex vivo brain tissue.