为斑马鱼幼鱼脑图谱建立可复现的隐式神经表征基准，评估不同编码方式对神经结构细节的还原能力。
Implicit neural representations for larval zebrafish brain microscopy: a reproducible benchmark on the MapZebrain atlas
- 采用统一种子控制协议，对比四种隐式神经编码在950张显微图像上的表现
- 哈尔和傅里叶编码在保留神经纤维边界方面最优，重建信噪比达约26 dB
- 哈尔与傅里叶编码适合边界敏感任务，SIREN适合作背景建模轻量基线
隐式神经表示（INRs）为图谱配准、跨模态重采样、稀疏视图补全及神经解剖数据紧凑共享提供了基于坐标的连续编码。然而，针对高分辨率斑马鱼幼鱼显微成像的可复现评估仍缺乏，其中保持神经元网络边界和细微神经过程至关重要。本文为MapZebrain斑马鱼幼鱼脑图谱构建了一个可复现的INR基准。通过统一且种子可控的流程，我们在950张灰度显微图像上对比了SIREN、傅里叶特征、哈尔位置编码和多分辨率网格的表现，包含图谱切片与单神经元投影。图像采用每幅图基于非保留列中10%像素估算的(1,99)百分位进行归一化，空间泛化性通过沿X轴确定性的40%列留出测试。哈尔与傅里叶编码在保留列上的宏观平均重建保真度最高（约26 dB），网格次之。SIREN在宏观平均上表现较差，但在全合一模式下的面积加权微观平均中仍具竞争力。SSIM与边缘聚焦误差进一步表明，哈尔与傅里叶编码更准确地保留了边界。结果表明，显式频域与多尺度编码相比平滑偏置型方法更能捕捉高频神经解剖细节。对于MapZebrain工作流，哈尔与傅里叶编码适用于边界敏感任务如图谱配准、标签迁移和形态保持共享，而SIREN仍可作为背景建模或去噪的轻量基线。
原文摘要 · Abstract (English)
Implicit neural representations (INRs) offer continuous coordinate-based encodings for atlas registration, cross-modality resampling, sparse-view completion, and compact sharing of neuroanatomical data. Yet reproducible evaluation is lacking for high-resolution larval zebrafish microscopy, where preserving neuropil boundaries and fine neuronal processes is critical. We present a reproducible INR benchmark for the MapZebrain larval zebrafish brain atlas. Using a unified, seed-controlled protocol, we compare SIREN, Fourier features, Haar positional encoding, and a multi-resolution grid on 950 grayscale microscopy images, including atlas slices and single-neuron projections. Images are normalized with per-image (1,99) percentiles estimated from 10% of pixels in non-held-out columns, and spatial generalization is tested with a deterministic 40% column-wise hold-out along the X-axis. Haar and Fourier achieve the strongest macro-averaged reconstruction fidelity on held-out columns (about 26 dB), while the grid is moderately behind. SIREN performs worse in macro averages but remains competitive on area-weighted micro averages in the all-in-one regime. SSIM and edge-focused error further show that Haar and Fourier preserve boundaries more accurately. These results indicate that explicit spectral and multiscale encodings better capture high-frequency neuroanatomical detail than smoother-bias alternatives. For MapZebrain workflows, Haar and Fourier are best suited to boundary-sensitive tasks such as atlas registration, label transfer, and morphology-preserving sharing, while SIREN remains a lightweight baseline for background modelling or denoising.
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