ADTAH: Neuron 3D Reconstruction Via Adaptive Distance Transformation and Adaptive Hessian Matrix

Three-dimensional (3D) reconstruction of neurons is a critical and evolving area in neuroscience, addressing the substantial challenges presented by weak signals, high noise levels, and heterogeneous signal distribution in neuronal optical images. Previous methodologies predominantly focused on reconstructing neuronal fibers but faced significant limitations in integrating both neuronal fibers and somas, making it difficult to handle large-scale neuronal image reconstruction. Furthermore, conventional techniques often employ fixed thresholding to eliminate background noise, inadvertently leading to the loss of valuable low-intensity neuronal signals, which are crucial for comprehensive neuronal analysis. In response to these limitations, we propose ADTAH, an innovative neuron image reconstruction method that leverages the adaptive distance transform combined with the Hessian matrix for robust and precise 3D reconstruction of neurons. ADTAH consists of two branches: nerve fiber reconstruction and soma reconstruction. The nerve fiber reconstruction branch begins with adaptive threshold distance transform, dynamically adjusting the Hessian matrix window size to effectively capture nerve fibers of varying thicknesses, thereby optimizing the extraction of tubular structures. The soma reconstruction branch employs high-threshold distance transform to accurately identify and fill somas, ensuring comprehensive coverage of both fibers and somas. Our experiments on publicly available 3D neuron image datasets demonstrate that ADTAH outperforms existing techniques, providing superior differentiation between neuronal and background signals, enhanced computational efficiency, and greater robustness. Our approach has demonstrated state-of-the-art performance on the publicly available 3D Neuron image dataset Big Neuron and an fMost dataset made available by the Allen Institute for Science.