一种用户友好的机器学习管道用于矩阵透镜体叶片自动分割。

IF 2.4 Q3 BIOCHEMICAL RESEARCH METHODS
Bioinformatics and Biology Insights Pub Date : 2025-06-08 eCollection Date: 2025-01-01 DOI:10.1177/11779322251344033
Michelle Lynn Yung, Kamila Murawska-Wlodarczyk, Alicja Babst-Kostecka, Raina Margaret Maier, Nirav Merchant, Aikseng Ooi
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引用次数: 0

摘要

自动化叶片分割管道必须平衡准确性、可扩展性和可用性,以便在植物研究中容易采用。我们提出了一个端到端的深度学习管道,设计用于植物表型的实际应用,我们在使用Atriplex lentiformis的真实植物生长实验中开发和评估了该管道。该管道集成了一个经过微调的基于Mask区域的卷积神经网络(Mask R-CNN)分割模型,该模型对176张植物图像进行了训练,尽管训练数据集很小(Dice系数= 0.781),但仍取得了很高的性能。我们定量地将微调后的Mask R-CNN模型与Meta AI的Segment Anything model (SAM)进行比较,并使用Grounded SAM和Leaf-Only SAM后处理管道来评估自然语言提示,以改进分割输出。我们的研究结果强调,在特定领域的任务中,在专门数据集上的迁移学习仍然可以优于大型基础模型。此外,我们集成了用于自动样本识别的QR码,并对多个QR码解码库进行了基准测试,评估了它们在失真和光照变化等现实成像条件下的鲁棒性。为了确保可访问性,我们将管道部署为用户友好的Streamlit web应用程序,允许研究人员在没有深度学习专业知识的情况下分析图像。通过关注模型性能之外的实际部署,本研究为植物科学应用提供了一个开源的、可扩展的框架,并解决了最终研究人员在自动化和可用性方面的现实挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation in Atriplex lentiformis.

Automated leaf segmentation pipelines must balance accuracy, scalability, and usability to be readily adopted in plant research. We present an end-to-end deep learning pipeline designed for practical use in plant phenotyping, which we developed and evaluated during a real-world plant growth experiment using Atriplex lentiformis. The pipeline integrates a fine-tuned Mask Region-based Convolutional Neural Network (Mask R-CNN) segmentation model trained on 176 plant images and achieves high performance despite the small training data set (Dice coefficient = 0.781). We quantitatively compare the fine-tuned Mask R-CNN model to Meta AI's Segment Anything Model (SAM) and evaluate natural language prompts using Grounded SAM and the Leaf-Only SAM post-processing pipeline for refining segmentation outputs. Our findings highlight that transfer learning on a specialized data set can still outperform a large foundation model in domain-specific tasks. In addition, we integrate QR codes for automated sample identification and benchmark multiple QR code decoding libraries, evaluating their robustness under real-world imaging conditions like distortion and lighting variation. To ensure accessibility, we deploy the pipeline as a user-friendly Streamlit web application, allowing researchers to analyze images without deep learning expertise. By focusing on practical deployment in addition to model performance, this study provides an open-source, scalable framework for plant science applications and addresses real-world challenges in automation and usability by the end-researcher.

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来源期刊
Bioinformatics and Biology Insights
Bioinformatics and Biology Insights BIOCHEMICAL RESEARCH METHODS-
CiteScore
6.80
自引率
1.70%
发文量
36
审稿时长
8 weeks
期刊介绍: Bioinformatics and Biology Insights is an open access, peer-reviewed journal that considers articles on bioinformatics methods and their applications which must pertain to biological insights. All papers should be easily amenable to biologists and as such help bridge the gap between theories and applications.
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