Apivanichkul, Kamonchat
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Preferred name
Apivanichkul, Kamonchat
Alternative Name
Apivanichkul, Miss Kamonchat
Main Affiliation
Email
kamonchat.ap@kmitl.ac.th
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Item type:Publication, Performance Comparison of Deep Learning Approach for Automatic CT Image Segmentation by Using Window Leveling(2021-01-01); ; Dankulchai, PittayaIn 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Effect of Preprocessing on U-Net for Bladder Segmentation in CT Images(2023-01-01); ; Dankulchai, PittayaThis 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation(2023-06-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CT Dataset Enhancement using Additional Feature Insertion for Automatic Femur Segmentation Model Based on Deep Learning(2022-01-01); ; Pittaya, DankulchaiThis paper proposed to insert additional feature into input datasets (i.e., CT scans) for automatic femur segmentation model, U-Net, with respect to increase the accuracy of model performance. An additional feature is available reference information representing identity on each CT scans and has an effect on results of deep learning model training. In this experiment, choose the left-femur as the target organ, which is common organs-At-risk (OARs) for lower abdominal cancers. The automatic femur segmentation model training was separately executed through two different datasets, one cropped-dataset with additional feature and one original dimension dataset without additional feature. For additional feature, lying posture of patient when entered the CT scanner was selected. The performance results of both trained U-Net models were compered in order to observe the difference of effect. Evaluation results reported that the additional feature could increase an accuracy and precision including support prediction for the left-femur segmentation, with the Dice Similarity Coefficient (DSC) of 61.573% and Intersection Over Union (IoU) of 45.621%, respectively. Specifically, deep learning combining additional feature insertion on cropped-datasets was the novelty in this experiment to effectively segment the left femur.