小鼠大脑组织学图像的自动分割

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Algorithms Pub Date : 2023-12-01 DOI:10.3390/a16120553
Juan Cisneros, Alain Lalande, Binnaz Yalcin, Fabrice Meriaudeau, Stephan Collins
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引用次数: 0

摘要

利用与国际小鼠表型联盟合作开发的脑组织组织的高通量神经解剖学筛选,我们先前报道了198个基因的失活导致神经解剖学表型的列表。为了实现这一里程碑,需要成千上万小时的人工图像分割。目前的工作涉及开发一个完整的流水线,以自动应用深度学习方法对上述屏幕中使用的24个解剖区域进行自动分割。该数据集包括2000张带注释的副矢状面幻灯片(24000 × 14000像素)。我们的方法包括三个主要部分:图像的转换(;ROI到。png),对压缩图像(深度学习方法的512 × 256和2048 × 1024像素)进行深度学习方法的训练,使用U-Net或Attention U-Net架构提取感兴趣的区域,最后将识别的区域(. png到。ROI)进行转换,从而在Fiji/ImageJ 1.54软件环境中实现可视化和编辑。在图像分辨率为2048 × 1024的情况下,Attention U-Net在24个区域的整体Dice Similarity Coefficient (DSC)为0.90±0.01,呈现出最佳效果。使用一个命令行,最终用户现在能够自动预分析图像,然后运行由ImageJ宏组成的现有分析管道来验证自动生成的感兴趣的区域。即使对于低DSC的区域,专家神经解剖学家也很少纠正结果。我们估计可以节省6到10倍的时间。
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Automatic Segmentation of Histological Images of Mouse Brains
Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of manual image segmentation were necessary. The present work involved developing a full pipeline to automate the application of deep learning methods for the automated segmentation of 24 anatomical regions used in the aforementioned screen. The dataset includes 2000 annotated parasagittal slides (24,000 × 14,000 pixels). Our approach consists of three main parts: the conversion of images (.ROI to .PNG), the training of the deep learning approach on the compressed images (512 × 256 and 2048 × 1024 pixels of the deep learning approach) to extract the regions of interest using either the U-Net or Attention U-Net architectures, and finally the transformation of the identified regions (.PNG to .ROI), enabling visualization and editing within the Fiji/ImageJ 1.54 software environment. With an image resolution of 2048 × 1024, the Attention U-Net provided the best results with an overall Dice Similarity Coefficient (DSC) of 0.90 ± 0.01 for all 24 regions. Using one command line, the end-user is now able to pre-analyze images automatically, then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. Even for regions with low DSC, expert neuroanatomists rarely correct the results. We estimate a time savings of 6 to 10 times.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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