Fast and Accurate Monomodal 3D High Resolution Deep Registration of Drosophila Larval Brain Volumes

2026-09-10

AI Summary

本文提出一种快速准确的深度学习方法，用于解决果蝇幼虫脑体积的高分辨率3D配准问题，相比传统方法更高效且鲁棒性强。

Abstract

The larval stage of Drosophila melanogaster is a compact model system for neuroscience whose genetic toolkit allows fluorescent markers to be expressed in defined neural populations, and comparing the resulting expression patterns across animals requires every brain to be registered into a shared anatomical reference space. Existing pipelines for this task are predominantly based on classical registration methods, which perform a new optimization for each volume, often require per-case parameter tuning, and can take minutes per brain, limiting their use as a routine preprocessing step. We present a trained deep registration pipeline that deformably aligns a larval brain to a reference template in a single forward pass at high spatial resolution, on volumes that hold several times more voxels than those learned 3D registration is normally reported on, together with the preprocessing and anatomy-anchored evaluation pipeline required to apply it. Against eleven classical and seven further learned baselines on a held-out collection acquired with different acquisition and quality strata, the proposed pipeline is the most accurate, improving on the strongest classical baseline by 23 percentage points of anatomical landmark-local mutual information. It registers a volume one to two orders of magnitude faster than the classical deformable pipelines, and it retains more of its accuracy than any other method as acquisition quality degrades. The network, its trained weights and the full pipeline are released as the open-source deep larval brain registration framework: https://github.com/agentdr1/deep-larval-brain-reg

Problem

• 3D Registration
• Drosophila Larval Brain
• High Resolution
• Deep Learning
• Anatomical Reference Space

Innovation

• deep registration
• high resolution
• fast alignment
• anatomical landmark-local mutual information
• open-source framework