用神经场统一展开复杂稀疏解剖结构，实现低失真二维可视化。
Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields
- 用神经场建模解剖结构到二维的映射变换。
- 峰值失真低于网格基方法，且变换更平滑。
- 适用于血管、导管等复杂稀疏结构，无需标注辅助目标。
断层成像可揭示三维物体内部结构，对医学诊断至关重要。可视化跨越多个二维切片的非平面稀疏解剖结构（如血管、导管、骨骼系统）的形态与外观极具挑战性，但对决策和报告极为重要。现有技术多为器官特异性展开方法，用于将密集采样的三维表面映射至畸变最小的二维表示。然而，尚无通用框架能处理复杂稀疏结构。本文采用神经场拟合目标解剖结构到二维概览图的变换，并提出畸变正则化策略，结合几何与强度损失，同时呈现未标注及辅助目标。相比基于网格的基线方法，本方法在稀疏结构上峰值畸变更低；所提正则化方案生成的变换比基于神经场图像配准的雅可比形式更平滑。
原文摘要 · Abstract (English)
Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Visualizing the morphology and appearance of non-planar sparse anatomical structures that extend over multiple 2D slices in tomographic volumes is inherently difficult but valuable for decision-making and reporting. Hence, various organ-specific unfolding techniques exist to map their densely sampled 3D surfaces to a distortion-minimized 2D representation. However, there is no versatile framework to flatten complex sparse structures including vascular, duct or bone systems. We deploy a neural field to fit the transformation of the anatomy of interest to a 2D overview image. We further propose distortion regularization strategies and combine geometric with intensity-based loss formulations to also display non-annotated and auxiliary targets. In addition to improved versatility, our unfolding technique outperforms mesh-based baselines for sparse structures w.r.t. peak distortion and our regularization scheme yields smoother transformations compared to Jacobian formulations from neural field-based image registration.
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