IDP-Head: An Interactive Dual-Perception Architecture for Organoid Detection in Mouse Microscopic Images.

IF 3.9 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Yuhang Yang, Changyuan Fan, Xi Zhou, Peiyang Wei
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Abstract

The widespread application of organoids in disease modeling and drug development is significantly constrained by challenges in automated quantitative analysis. In bright-field microscopy images, organoids exhibit complex characteristics, including irregular morphology, blurred boundaries, and substantial scale variations, largely stemming from their dynamic self-organization that mimics in vivo tissue development. Existing convolutional neural network-based methods are limited by fixed receptive fields and insufficient modeling of inter-channel relationships, making them inadequate for detecting such evolving biological structures. To address these challenges, we propose a novel detection head, termed Interactive Dual-Perception Head (IDP-Head), inspired by hierarchical perception mechanisms in the biological visual cortex. Integrated into the RTMDet framework, IDP-Head comprises two bio-inspired components: a Large-Kernel Global Perception Module (LGPM) to capture global morphological dependencies, analogous to the wide receptive fields of cortical neurons, and a Progressive Channel Synergy Module (PCSM) that models inter-channel semantic collaboration, echoing the integrative processing of multi-channel stimuli in neural systems. Additionally, we construct a new organoid detection dataset to mitigate the scarcity of annotated data. Extensive experiments on both our dataset and public benchmarks demonstrate that IDP-Head achieves a 5-percentage-point improvement in mean Average Precision (mAP) over the baseline model, offering a biologically inspired and effective solution for high-fidelity organoid detection.

IDP-Head:用于小鼠显微图像中类器官检测的交互式双感知架构。
类器官在疾病建模和药物开发中的广泛应用受到自动化定量分析挑战的极大限制。在明场显微镜图像中,类器官表现出复杂的特征,包括不规则的形态、模糊的边界和大量的尺度变化,这主要源于它们模仿体内组织发育的动态自组织。现有的基于卷积神经网络的方法受到固定的感受野和通道间关系建模不足的限制,使得它们不足以检测这种进化的生物结构。为了解决这些挑战,我们提出了一种新的检测头,称为交互式双感知头(IDP-Head),灵感来自生物视觉皮层的分层感知机制。集成到RTMDet框架中,IDP-Head包括两个生物启发组件:一个大核全局感知模块(LGPM),用于捕获全局形态学依赖性,类似于皮质神经元的广泛接受场;一个逐步通道协同模块(PCSM),用于模拟通道间语义协作,响应神经系统中多通道刺激的综合处理。此外,我们构建了一个新的类器官检测数据集,以缓解标注数据的稀缺性。在我们的数据集和公共基准上进行的大量实验表明,IDP-Head在平均平均精度(mAP)方面比基线模型提高了5个百分点,为高保真类器官检测提供了生物学启发和有效的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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