Novel 3D Binary Indexed Tree for Volume Computation of 3D Reconstructed Models from Volumetric Data

In the burgeoning field of medical imaging, precise computation of 3D volume holds a significant importance for subsequent qualitative analysis of 3D reconstructed objects. Combining multivariate calculus, marching cube algorithm, and binary indexed tree data structure, we developed an algorithm for efficient computation of intrinsic volume of any volumetric data recovered from computed tomography (CT) or magnetic resonance (MR). We proposed the 30 configurations of volume values based on the polygonal mesh generation method. Our algorithm processes the data in scan-line order simultaneously with reconstruction algorithm to create a Fenwick tree, ensuring query time much faster and assisting users' edition of slicing or transforming model. We tested the algorithm's accuracy on simple 3D objects (e.g., sphere, cylinder) to complicated structures (e.g., lungs, cardiac chambers). The result deviated within $\pm 0.004 \text{cm}^3$ and there is still room for further improvement.

RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation

Cardiovascular disease is a major global health concern, contributing significantly to global mortality. Accurately segmenting cardiac medical imaging data is crucial for reducing fatality rates associated with these conditions. However, current state-of-the-art (SOTA) neural networks, including CNN-based and Transformer-based approaches, face challenges in capturing both inter-slice connections and intra-slice details, especially in datasets featuring intricate, long-range details along the z-axis like coronary arteries. Existing methods also struggle with differentiating non-cardiac components from the myocardium, resulting in segmentation inaccuracies and the "spraying" phenomenon. To address these issues, we introduce RotCAtt-TransUNet++, a novel architecture designed for robust segmentation of intricate cardiac structures. Our approach enhances global context modeling through multiscale feature aggregation and nested skip connections in the encoder. Transformer layers facilitate capturing intra-slice interactions, while a rotatory attention mechanism handles inter-slice connectivity. A channel-wise cross-attention gate integrates multiscale information and decoder features, effectively bridging semantic gaps. Experimental results across multiple datasets demonstrate superior performance over current methods, achieving near-perfect annotation of coronary arteries and myocardium. Ablation studies confirm that our rotatory attention mechanism significantly improves segmentation accuracy by transforming embedded vectorized patches in semantic dimensional space.