Phasukkit, Pattarapong

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Phasukkit, Pattarapong
Alternative Name
Phasukkit, P.
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Email
pattarapong.ph@kmitl.ac.th

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    Item type:Publication,
    Feasibility of Prediction Model for Internal Tumor Target Volume from 4-D Computed Tomography of Lung cancer
    (2021-01-01)
    Puangragsa, Utumporn
    ;
    Lomvisai, Pitchayakorn
    ;
    Phasukkit, Pattarapong
    ;
    Puangragsa, Sarut
    ;
    Setakornnukul, Jiraporn
    4-Dimensional computed tomography (4DCT) is the most common technique to determine organ movement due to breathing motion. However, the ability of 4DCT to acquire CT images as a function of the respiratory phase increases higher radiation dose. To reduce the patient's radiation dose, this study created lung motion prediction models used to estimate tumor target movement in ten respiratory phases by detecting only external organ movement during a complete respiration cycle without radiation with Kinect. The average overall amplitude difference between RPM and Kinect signals in the phantom experiment was 0.02 ± 0.1 mm. F1 score of 100% for all most all classifications except classification 2,3,6,7 and 8 of 85%,83%,90%, 84%,85% where irregular breathing pattern. Essentially, the proposed tumor movement scheme's total accuracy (average of F1 scores) is 92.7 %. Deep learning model can predict tumor motion range and classification zone by used detection of the external respiratory signal
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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.