Apivanichkul, Kamonchat

Loading...
Thumbnail Image
Preferred name
Apivanichkul, Kamonchat
Alternative Name
Apivanichkul, Miss Kamonchat
Main Affiliation
Email
kamonchat.ap@kmitl.ac.th

Settings

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Performance Comparison of Deep Learning Approach for Automatic CT Image Segmentation by Using Window Leveling
    (2021-01-01)
    Apivanichkul, Kamonchat
    ;
    Phasukkit, Pattarapong
    ;
    Dankulchai, Pittaya
    In tumor radiotherapy process, radiologist need to make multipleorgans contouring on medical images such as CT scans for computing appropriate dose and making a suitable treatment plan for patients. This is a necessary step before treatment. This paper was written to be one of automatic image segmentation research by using deep learning. The experiment compared performance between preprocessing input datasets with custom window leveling normalization and following by organ types. We chose the bladder, the rectum and the femur as target organs in this paper. Datasets are directly obtained from Siriraj Hospital that contoured by radiologists. There are 10 datasets of each organs. We used U-Net as main structure to extract features on image then evaluated by dice similarity coefficient (DSC) and intersection over union (IoU). The experiment resulted that training with custom window leveling normalization is better performance. The bladder got DSC and IoU of 78.34% and 70.46%, femur were 39.71% and 28.03%, and rectum were 19.19% and 12.20%, respectively.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    The Effect of Preprocessing on U-Net for Bladder Segmentation in CT Images
    (2023-01-01)
    Apivanichkul, Kamonchat
    ;
    Phasukkit, Pattarapong
    ;
    Dankulchai, Pittaya
    This research proposes preprocessing techniques for computed tomography (CT) slices with the aim of improving the performance of a deep-learning segmentation model (U-Net model). The preprocessing techniques used in this study include window leveling, histogram equalization, Gaussian blurring, and cropping (incorporating mathematical morphology and histogram projection). The U-Net model is applied to three groups of input data sets (B-I to B-III) for training, validation, and testing. The segmentation performance is evaluated using metrics such as Dice similarity coefficient (DSC) and intersection over union (IoU). The trained U-Net model achieves the highest DSC (95.15%) and IoU (91.09%) under B-III dataset, utilizing cropped and enhanced CT input datasets. Window leveling, histogram equalization, Gaussian blurring, and cropping are identified as the optimal preprocessing techniques for bladder segmentation. This novel research applies diverse image processing techniques in medical image preprocessing to enhance the deep-learning U-Net model's segmentation performance.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    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.