In widefield fluorescence imaging, out-of-focus and scattered light emanating from the cell body frequently obscures nearby dim fibers and degrades their contrast. Scanning techniques can ameliorate this issue but are hindered by a slower imaging speed and higher cost. We dramatically reduce stray light in widefield imaging by directing illumination primarily to neuron fibers. We identify fibers through real-time iterative image processing and pattern illumination onto these fibers using a digital micromirror device in a standard widefield microscope. By illuminating bright cell bodies with minimal light and in-focus fibers with high light intensity, we diminish the background and enhance the fibers' visibility. This methodology retains a high imaging speed and remains cost-effective. Employing this targeted illumination strategy, we have achieved confocal quality imaging of complex neurons in anesthetized C. elegans, ex vivo mouse brain slices, and restrained zebrafish larvae.
Analyses of biomedical images often rely on accurate segmentation of structures of interest. Traditional segmentation methods based on thresholding, watershed, fast marching, and level set perform well in high-contrast images containing structures of similar intensities. However, such methods can under-segment or miss entirely low-intensity objects on noisy backgrounds. Machine learning segmentation methods promise superior performance but require large training datasets of labeled images which are difficult to create, particularly in 3D. Here, we propose an algorithm based on the Local Binary Fitting level set method and its application specifically designed to improve the segmentation accuracy for low-contrast structures even with significant noise levels present. The proposed algorithm, the Normalized Local Binary Fitting level set method, shows promise in enhancing the segmentation of low-contrast structures in biomedical images, addressing the limitations of traditional segmentation methods, and offering an alternative to machine learning approaches that require extensive training datasets.
Out-of-focus and scattered light often obscure dim fluorescent fibers in widefield neuronal imaging. We solve this obstacle by spatially modulating the excitation light to illuminate different neuronal structures with different light intensities. In this way, we minimize background and enhance the visibility of neuron fibers in the final image. This method significantly improves fiber contrast while maintaining a fast imaging speed and low cost. Using this widefield targeted illumination setup, we demonstrate confocal quality imaging of complex neurons in the nematode C. elegans.
Unwanted background fluorescence in microscopy can occur when light from fluorescent structures scatters against nearby tissues. Bright cell bodies produce haze that mask nearby dim structures, including neuronal fibers. We have developed a method to eliminate this haze by fitting it to a simple function. This method enables clearer post-imaging visualization of axons in our in vivo imaging of neurons.
We are investigating ways to increase the computation speed of our background fitting technique to allow for real-time image improvement. The implementation of our method in real-time imaging will broaden the potential applications of this fitting method.
Stray light, including scattered and out-of-focus light, can obscure the imaging of dim structures. We added a spatial light modulator at the field stop of our widefield fluorescence microscope to spatially control the illumination and are pursuing several approaches to reduce stray light. We demonstrate confocal capability and a combined >50x signal-to-background ratio improvement by using existing and novel techniques in illumination modulation and image postprocessing. Our approaches offer a simple and low-cost method for adapting existing microscopes to greatly improve the visibility of dim structures that are obscured by bright neighbors.
The nematode C. elegans, a millimeter-long roundworm, is a well-established model organism for studies of neural development and behavior, however physiological methods to manipulate and monitor the activity of its neural network have lagged behind the development of powerful methods in genetics and molecular biology. The small size and transparency of C. elegans make the worm an ideal test-bed for the development of physiological methods derived from optics and microscopy. We present the development and application of a new physiological tool: femtosecond laser dissection, which allows us to selectively ablate segments of individual neural fibers within live C. elegans. Femtosecond laser dissection provides a scalpel with submicrometer resolution, and we discuss its application in studies of neural growth, regenerative growth, and the neural basis of behavior.
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