Apivanichkul, Kamonchat

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Apivanichkul, Kamonchat
Alternative Name
Apivanichkul, Miss Kamonchat
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Email
kamonchat.ap@kmitl.ac.th

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    Item type:Publication,
    Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation
    (2023-06-01)
    Apivanichkul, Kamonchat
    ;
    Phasukkit, Pattarapong
    ;
    Dankulchai, Pittaya
    ;
    Sittiwong, Wiwatchai
    ;
    Jitwatcharakomol, Tanun
    This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur model. In the study, the deep-learning-based automatic left-femur segmentation scheme was trained, validated, and tested using eight categories of CT input datasets for the left femur (F-I–F-VIII). The segmentation performance was assessed by Dice similarity coefficient (DSC) and intersection over union (IoU); and the similarity between the predicted 3D reconstruction images and ground-truth images was determined by spectral angle mapper (SAM) and structural similarity index measure (SSIM). The left-femur segmentation model achieved the highest DSC (88.25%) and IoU (80.85%) under category F-IV (using cropped and augmented CT input datasets with large feature coefficients), with an SAM and SSIM of 0.117–0.215 and 0.701–0.732. The novelty of this research lies in the use of attribute augmentation in medical image preprocessing to enhance the performance of the deep-learning-based automatic left-femur segmentation scheme.
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    Lightweight Transformer-Based Efficient Two-Step Network for Temporal Action Segmentation in Ultralow Frame Rate Excavator Work Videos at the Construction Site
    (2026-01-01)
    Sereepookkana, Kanok
    ;
    Orachon, Teerapong
    ;
    Doi, Shigeo
    ;
    Apivanichkul, Kamonchat
    ;
    Itayama, Yuichiro
    In this paper, we propose a novel lightweight Transformer-based architecture for Temporal Action Segmentation (TAS) in an ultralow frame rate video setting, called a Transformer-Based Efficient Two-Step Network (ETSNFormer). This study makes three main contributions to the literature. First, we demonstrate that the choice of the temporal window size for feature extraction significantly affects the segmentation performance in an ultralow frame rate video setting. Second, we enhance the Efficient Two-Step Network (ETSN) baseline by integrating a modified Transformer-based decoder block, yielding improved segmentation accuracy while using fewer computational resources. Third, we employ a genetic algorithm-based hyperparameter-Tuning approach to automatically tune the hyperparameters of Local Burr Suppression (LBS), which is the post-processing method used in the ETSN baseline to mitigate the over-segmentation problem. On our ultralow frame rate excavator video dataset, ETSNFormer achieved state-of-The-Art results while using fewer parameters than prior approaches: A frame-wise accuracy of 89.91%, an edit score of 72.09%, and segmental F1 scores of 79.11%, 78.42%, and 73.51% at thresholds of 0.1, 0.25, and 0.5, respectively.