arXiv subjects

Manmohan Singh

Publications and source records attributed to Manmohan Singh.

2 recordsLinked to original sources

arXiv2026

Fully-Automated Multi-Modal Registration of Timelapse Zebrafish OCT+LS Images

Optical coherence tomography (OCT) and light sheet (LS) fluorescence microscopy provide complementary views of tissue morphology and molecular expression, offering a powerful approach for studying embryonic development. However, longitudinal integration of OCT+LS images is challenging because the modalities differ substantially in contrast, spatial resolution, field of view, and visible anatomical features, while embryo growth and movement introduce additional misalignment over time. These challenges stem from both the scale and multimodal nature of the OCT and LS image stacks. We present a fully automated three-dimensional rigid registration framework for aligning OCT and LS volumes of developing zebrafish embryos acquired from 21 to 48 hours post fertilization (HPF). The method does not require pre-existing segmentations or cross-modal feature correspondences, which are often unreliable because OCT and LS capture fundamentally different tissue properties. Registration performance was evaluated across developmental time points using mean target registration error (mTRE) and compared across multiple similarity metrics and optimization methods. Normalized cross-correlation combined with particle swarm optimization produced the most accurate and consistent registration, achieving the lowest mTRE at every evaluated time point compared with the alternative methods. The registration establishes spatial correspondence between OCT tissue morphology and LS fluorescence signals throughout development, enabling integrated longitudinal analysis of structural and molecular changes in living embryos.

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arXiv2021

Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

Optical Coherence Tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical set-up and can be easily integrated with existing swept-source or spectral domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in ~6.73 ms using a desktop computer, removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3x undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2x spectral undersampling. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.

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