Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods.
In order to accurately monitor neural activity in a living mouse brain, it is necessary to image each neuron at a high frame rate. Newly developed genetically encoded calcium indicators like GCaMP6 have fast kinetic response and can be used to target specific cell types for long duration. This enables neural activity imaging of neuron cells with high frame rate via fluorescence microscopy. In fluorescence microscopy, a laser scans the whole volume and the imaging time is proportional to the volume of the brain scanned. Scanning the whole brain volume is time consuming and fails to fully exploit the fast kinetic response of new calcium indicators. One way to increase the frame rate is to image only the sparse set of voxels containing the neurons. However, in order to do this, it is necessary to accurately detect and localize the position of each neuron during the data acquisition. In this paper, we present a novel model-based neuron detection algorithm using sparse location priors. We formulate the neuron detection problem as an image reconstruction problem where we reconstruct an image that encodes the location of the neuron centers. We use a sparsity based prior model since the neuron centers are sparsely distributed in the 3D volume. The information about the shape of neurons is encoded in the forward model using the impulse response of a filter and is estimated from training data. Our method is robust to illumination variance and noise in the image. Furthermore, the cost function to minimize in our formulation is convex and hence is not dependent on good initialization. We test our method on GCaMP6 fluorescence neuron images and observe better performance than widely used methods.
Self-interference incoherent digital holography (SIDH) technique allows to capture the complex wavefront information with incoherent light. We suggest a distortion compensation method in an incoherent microscope system using SIDH. To correct the distorted wavefront, we detect the distorted wavefront of a guide-star with SIDH method and modulate the wavefront with a phase-only spatial light modulator (SLM). We present an iterative algorithm that calculates the SLM modulation pattern to correct the wavefront. By the correction, the size of focused spot becomes smaller. It means that the resolution of the image can be improved or even an object covered with a thick aberration layer can be detectable. We verify the feasibility of the wavefront correction method with the simulation and experimental results.