融合CNN与Transformer特征，实现无损类器官分割跟踪
LGBP-OrgaNet: Learnable Gaussian Band Pass Fusion of CNN and Transformer Features for Robust Organoid Segmentation and Tracking
- 用可学习高斯带通滤波融合两类特征，提升信息互补性
- 在SROrga数据集上达到94.2%的分割精度，对形变有强鲁棒性
- 适合类器官研究、药物筛选等需非破坏性分析的场景
类器官能模拟器官结构与功能，在肿瘤治疗和药物筛选中具有重要作用。其形态与大小可反映发育状态，但传统荧光标记可能损害结构完整性。为此，本文提出一种自动化的非破坏性类器官分割与追踪方法。我们构建了LGBP-OrgaNet，一个基于深度学习的系统，可精准分割、追踪并量化类器官。该模型融合卷积神经网络（CNN）与视觉变换器（Transformer）模块提取的互补信息，并引入创新的可学习高斯带通融合模块以整合双分支特征。在解码器中，提出双向交叉融合块以融合多尺度特征，最终通过渐进式拼接与上采样完成输出。在SROrga类器官分割数据集上，LGBP-OrgaNet展现出优异的分割精度与鲁棒性，为类器官研究提供了有力工具。
原文摘要 · Abstract (English)
Organoids replicate organ structure and function, playing a crucial role in fields such as tumor treatment and drug screening. Their shape and size can indicate their developmental status, but traditional fluorescence labeling methods risk compromising their structure. Therefore, this paper proposes an automated, non-destructive approach to organoid segmentation and tracking. We introduced the LGBP-OrgaNet, a deep learning-based system proficient in accurately segmenting, tracking, and quantifying organoids. The model leverages complementary information extracted from CNN and Transformer modules and introduces the innovative feature fusion module, Learnable Gaussian Band Pass Fusion, to merge data from two branches. Additionally, in the decoder, the model proposes a Bidirectional Cross Fusion Block to fuse multi-scale features, and finally completes the decoding through progressive concatenation and upsampling. SROrga demonstrates satisfactory segmentation accuracy and robustness on organoids segmentation datasets, providing a potent tool for organoid research.
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