In human brain, Corpus Callosum (CC) is the largest white matter structure, connecting between right and left hemispheres. Structural features such as shape and size of CC in midsagittal plane are of great significance for analyzing various neurological diseases, for example Alzheimer’s disease, autism and epilepsy. For quantitative and qualitative studies of CC in brain MR images, robust segmentation of CC is important. In this paper, we present a novel method for CC segmentation. Our approach is based on deep neural networks and the prior information generated from multi-atlas images. Deep neural networks have recently shown good performance in various image processing field. Convolutional neural networks (CNN) have shown outstanding performance for classification and segmentation in medical image fields. We used convolutional neural networks for CC segmentation. Multi-atlas based segmentation model have been widely used in medical image segmentation because atlas has powerful information about the target structure we want to segment, consisting of MR images and corresponding manual segmentation of the target structure. We combined the prior information, such as location and intensity distribution of target structure (i.e. CC), made from multi-atlas images in CNN training process for more improving training. The CNN with prior information showed better segmentation performance than without.
We propose a new method for three-dimensional fluorescence imaging without depth scanning that we refer to as the
dual detection confocal fluorescence microscopy (DDCFM). Compared to conventional beam-scanning confocal
fluorescence microscopy, where the focal spot must be scanned either optically or mechanically to collect a three-dimensional
images, DDCFM is able to obtain depth information without depth scanning. DDCFM utilizes two photo
multiplier tubes (PMTs) in the confocal detection system. The emitted fluorescence is divided by the beam splitter and
received by the two PMTs through pinholes with different size. Each PMT signal generates different axial response
curve according to the pinhole diameter, which decides stiffness of the curve. Since the PMT signal is determined by the
intensity of the fluorescent emitter and the distance from the focal point, we can acquire depth position of a fluorescent
emitter by comparing two intensity signals from the PMTs. Since the depth information can be obtained by a single
excitation without depth scanning, DDCFM has many advantages. The measurement time is dramatically reduced for
volume imaging. Also, photo-bleaching and photo-toxicity can be minimized. The system can be easily miniaturized
because no mechanical depth scan is needed. Here, we demonstrate the feasibility of the proposed method by phantom
study using fluorescent beads.
In earlier work (KIM, J.S, MBEC, 2003), we demonstrated the registration method with a non-linear transformation using intensity similarity and feature similarity. Although the former approach showed good match in global shape of brain and feature-defined region, method contains user interventions for defining appropriate and sufficient number features. While manual delineating the region of interests for sufficient number of feature is a very time-consuming and can provide intra-, inter-rater variability, we proposed fully automatic hybrid registration via automatic feature defining method. Automatic feature definition was performed on the cortical surface from CLASP (KIM, J.S, Neuroimage, 2005) with using cortical surface matching algorithm (Robbins, S., MIA, 2004) and then applied to hybrid registration. The object of this work is to develop fully automated hybrid registration method which reveals enhanced performance in comparison to previous automated registration methods. In the result, our proposed scheme showed efficient performance from maintaining the strong points of hybrid registration without any user intervention.