Taryn Saggese’s research while affiliated with Medical Research Institute of New Zealand and other places

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Publications (1)


Arrangement of mature Pinus radiata somatic embryos on Petri plate with germination medium on the day of collection.
Annotated image of a Pinus radiata somatic embryo. The individual cotyledon instances make up the cotyledon region for semantic segmentation (outlined in green). The entire lower region is classified here as hypocotyl (outlined in pink).
Deep learning workflow for Pinus radiata somatic embryo segmentation. Images are captured under a high-resolution microscope before being manually annotated to train and evaluate the two neural networks. For Mask R-CNN instance segmentation, cotyledon instance predictions are combined to derive a segmentation mask for direct comparison of pixel-wise metrics with ResNet semantic segmentation. Additionally, individual instances detected in boxes allow for cotyledon counts to be derived and a range of performance metrics are evaluated.
Examples of ground truth segmentation mask, predicted mask, and both together applied to images of mature somatic embryos of Pinus radiata. False Positive (FP), True Positive (TP) and False Negative (FN) were used to compute IoU.
Examples of segmentation mask predictions compared with manual annotation of Pinus radiata somatic embryos (on original colour images) for both semantic segmentation and postprocessed instance segmentation masks for two embryos from each of the three cell lines (A–C) respectively.

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Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in mature Pinus radiata D. Don. somatic embryo images
  • Article
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March 2024

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1 Citation

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Taryn Saggese

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In commercial forestry and large-scale plant propagation, the utilization of artificial intelligence techniques for automated somatic embryo analysis has emerged as a highly valuable tool. Notably, image segmentation plays a key role in the automated assessment of mature somatic embryos. However, to date, the application of Convolutional Neural Networks (CNNs) for segmentation of mature somatic embryos remains unexplored. In this study, we present a novel application of CNNs for delineating mature somatic conifer embryos from background and residual proliferating embryogenic tissue and differentiating various morphological regions within the embryos. A semantic segmentation CNN was trained to assign pixels to cotyledon, hypocotyl, and background regions, while an instance segmentation network was trained to detect individual cotyledons for automated counting. The main dataset comprised 275 high-resolution microscopic images of mature Pinus radiata somatic embryos, with 42 images reserved for testing and validation sets. The evaluation of different segmentation methods revealed that semantic segmentation achieved the highest performance averaged across classes, achieving F1 scores of 0.929 and 0.932, with IoU scores of 0.867 and 0.872 for the cotyledon and hypocotyl regions respectively. The instance segmentation approach demonstrated proficiency in accurate detection and counting of the number of cotyledons, as indicated by a mean squared error (MSE) of 0.79 and mean absolute error (MAE) of 0.60. The findings highlight the efficacy of neural network-based methods in accurately segmenting somatic embryos and delineating individual morphological parts, providing additional information compared to previous segmentation techniques. This opens avenues for further analysis, including quantification of morphological characteristics in each region, enabling the identification of features of desirable embryos in large-scale production systems. These advancements contribute to the improvement of automated somatic embryogenesis systems, facilitating efficient and reliable plant propagation for commercial forestry applications.

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Citations (1)


... the photos collected in practical applications contain complex backgrounds such as soil and weeds, and there is also the problem of occlusion, which can lead to a decrease in classification accuracy. Therefore, it is more practical to use complex background image data for model training Davidson et al., 2024). With the rapid development of UAVs technology and spectral technology, UAVs have attracted the attention of many scholars by showing convenience and safety in the application of large-scale detection of plant pests Sato et al;Zhang et al., 2024). ...

Reference:

Residual swin transformer for classifying the types of cotton pests in complex background
Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in mature Pinus radiata D. Don. somatic embryo images