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A deep learning-based micro-CT image analysis pipeline for nondestructive quantification of the maize kernel internal structure

文献类型： 外文期刊

作者： Wang, Juan 1 ; Yang, Si 2 ; Wang, Chuanyu 2 ; Wen, Weiliang 2 ; Zhang, Ying 2 ; Liu, Gui 1 ; Li, Jingyi 4 ; Guo, Xinyu 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plants, Beijing 100097, Peoples R China

4.Southwest Minzu Univ, Coll Comp Sci & Engn, Chengdu 610041, Sichuan, Peoples R China

关键词： Maize kernel; Vitreous endosperm; Starchy endosperm; Semantic segmentation; Mirco-CT

期刊名称：PLANT PHENOMICS （ 影响因子：6.4； 五年影响因子：7.1 ）

ISSN： 2643-6515

年卷期： 2025 年 7 卷 1 期

页码：

收录情况： SCI

摘要： Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis. However, the complex internal structure of maize kernels presents several challenges. In CT (computed tomography) images, the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct, potentially leading to low segmentation accuracy or oversegmentation. Moreover, the blurred edges between the vitreous and starchy endosperm make segmentation difficult, often resulting in jagged segmentation outcomes. We propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds. First, CT images are acquired using a multislice CT scanner. To improve the efficiency of maize kernel CT imaging, a batch scanning method is used. Individual kernels are accurately segmented from batch-scanned CT images using the Canny algorithm. Second, we modify the conventional architecture for highquality segmentation of the vitreous and starchy endosperm in maize kernels. The conventional U-Net is modified by integrating the CBAM (convolutional block attention module) mechanism in the encoder and the SE (squeezeand-excitation attention) mechanism in the decoder, as well as by using the focal-Tversky loss function instead of the Dice loss, and the boundary smoothing term is weighted as an additional loss term, named CSFTU-Net. The experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy endosperm. Finally, a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture, including the volume of the kernel (V), volume of the vitreous endosperm (VV), volume of starchy endosperm (SV), and ratios over their respective total kernel volumes (VV/V and SV/V). The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels, offering valuable insights for maize breeding and processing.

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