var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=https%3A%2F%2Fraw.githubusercontent.com%2Favitech-vnu%2FAVITECH_website%2Fmain%2Fbibs%2FLeQuocAnh.bib&jsonp=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=https%3A%2F%2Fraw.githubusercontent.com%2Favitech-vnu%2FAVITECH_website%2Fmain%2Fbibs%2FLeQuocAnh.bib&jsonp=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=https%3A%2F%2Fraw.githubusercontent.com%2Favitech-vnu%2FAVITECH_website%2Fmain%2Fbibs%2FLeQuocAnh.bib&jsonp=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2024\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n New approach for Alzheimer’s disease classification using topographic maps and deep learning model.\n \n \n \n\n\n \n Le, Q. A.; and Nguyen, H. T.\n\n\n \n\n\n\n In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pages 1-6, 2024. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@INPROCEEDINGS{10848697,\nauthor={Le, Quoc Anh and Nguyen, Hong Thinh},\nbooktitle={2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)}, \ntitle={New approach for Alzheimer’s disease classification using topographic maps and deep learning model}, \nyear={2024},\nvolume={},\nnumber={},\npages={1-6},\nkeywords={Deep learning;Visualization;Power system measurements;Density measurement;Information processing;Inspection;Feature extraction;Electroencephalography;Recording;Alzheimer's disease},\ndoi={10.1109/APSIPAASC63619.2025.10848697}\n}\n\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Scalable Point Cloud Coding for Reconstruction and Classification.\n \n \n \n\n\n \n Le, Q. A.; Le, V. H.; Larabi, M.; and Valenzise, G.\n\n\n \n\n\n\n In International Workshop on ADVANCEs in ICT Infrastructures and Services, 2024. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{le2024scalable,\n  title={Scalable Point Cloud Coding for Reconstruction and Classification},\n  author={Le, Quoc Anh and Le, Vu Ha and Larabi, Mohamed-Chaker and Valenzise, Giuseppe},\n  booktitle={International Workshop on ADVANCEs in ICT Infrastructures and Services},\n  year={2024}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Precise ablation zone segmentation on CT images after liver cancer ablation using semi-automatic CNN-based segmentation.\n \n \n \n\n\n \n Le, Q. A.; Pham, X. L.; van Walsum, T.; Dao, V. H.; Le, T. L.; Franklin, D.; Moelker, A.; Le, V. H.; Trung, N. L.; and Luu, M. H.\n\n\n \n\n\n\n Medical Physics, 51(12): 8882–8899. 2024.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{le2024precise,\n  title={Precise ablation zone segmentation on CT images after liver cancer ablation using semi-automatic CNN-based segmentation},\n  author={Le, Quoc Anh and Pham, Xuan Loc and van Walsum, Theo and Dao, Viet Hang and Le, Tuan Linh and Franklin, Daniel and Moelker, Adriaan and Le, Vu Ha and Trung, Nguyen Linh and Luu, Manh Ha},\n  journal={Medical Physics},\n  volume={51},\n  number={12},\n  pages={8882--8899},\n  year={2024},\n  publisher={Wiley Online Library}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2023\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Impact of Image Denoising Techniques on CNN-based Liver Vessel Segmentation using Synthesis Low-dose Contrast Enhanced CT Images.\n \n \n \n\n\n \n Anh, L. Q.; Loc, P. X.; and Ha, L. M.\n\n\n \n\n\n\n REV Journal on Electronics and Communications, 12(3-4). 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{anh2023impact,\n  title={Impact of Image Denoising Techniques on CNN-based Liver Vessel Segmentation using Synthesis Low-dose Contrast Enhanced CT Images},\n  author={Anh, Le Quoc  and Loc, Pham Xuan and Ha, Luu Manh},\n  journal={REV Journal on Electronics and Communications},\n  volume={12},\n  number={3-4},\n  year={2023}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration.\n \n \n \n \n\n\n \n Luu, M. H.; Mai, H. S.; Pham, X. L.; Le, Q. A.; Le, Q. K.; van Walsum, T.; Le, N. H.; Franklin, D.; Le, V. H.; Moelker, A.; Chu, D. T.; and Trung, N. L.\n\n\n \n\n\n\n Computer Methods and Programs in Biomedicine, 233: 107453. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"QuantificationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{LUU2023107453,\ntitle = {Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration},\njournal = {Computer Methods and Programs in Biomedicine},\nvolume = {233},\npages = {107453},\nyear = {2023},\nissn = {0169-2607},\ndoi = {https://doi.org/10.1016/j.cmpb.2023.107453},\nurl = {https://www.sciencedirect.com/science/article/pii/S0169260723001190},\nauthor = {Manh Ha Luu and Hong Son Mai and Xuan Loc Pham and Quoc Anh Le and Quoc Khanh Le and Theo van Walsum and Ngoc Ha Le and Daniel Franklin and Vu Ha Le and Adriaan Moelker and Duc Trinh Chu and Nguyen Linh Trung},\nkeywords = {Liver-lung shunt, Liver cancer, SPECT/CT, CNNs, Segmentation, Registration},\nabstract = {Purpose: Selective internal radiation therapy (SIRT) has been proven to be an effective treatment for hepatocellular carcinoma (HCC) patients. In clinical practice, the treatment planning for SIRT using 90Y microspheres requires estimation of the liver-lung shunt fraction (LSF) to avoid radiation pneumonitis. Currently, the manual segmentation method to draw a region of interest (ROI) of the liver and lung in 2D planar imaging of 99mTc-MAA and 3D SPECT/CT images is inconvenient, time-consuming and observer-dependent. In this study, we propose and evaluate a nearly automatic method for LSF quantification using 3D SPECT/CT images, offering improved performance compared with the current manual segmentation method. Methods: We retrospectively acquired 3D SPECT with non-contrast-enhanced CT images (nCECT) of 60 HCC patients from a SPECT/CT scanning machine, along with the corresponding diagnostic contrast-enhanced CT images (CECT). Our approach for LSF quantification is to use CNN-based methods for liver and lung segmentations in the nCECT image. We first apply 3D ResUnet to coarsely segment the liver. If the liver segmentation contains a large error, we dilate the coarse liver segmentation into the liver mask as a ROI in the nCECT image. Subsequently, non-rigid registration is applied to deform the liver in the CECT image to fit that obtained in the nCECT image. The final liver segmentation is obtained by segmenting the liver in the deformed CECT image using nnU-Net. In addition, the lung segmentations are obtained using 2D ResUnet. Finally, LSF quantitation is performed based on the number of counts in the SPECT image inside the segmentations. Evaluations and Results: To evaluate the liver segmentation accuracy, we used Dice similarity coefficient (DSC), asymmetric surface distance (ASSD), and max surface distance (MSD) and compared the proposed method to five well-known CNN-based methods for liver segmentation. Furthermore, the LSF error obtained by the proposed method was compared to a state-of-the-art method, modified Deepmedic, and the LSF quantifications obtained by manual segmentation. The results show that the proposed method achieved a DSC score for the liver segmentation that is comparable to other state-of-the-art methods, with an average of 0.93, and the highest consistency in segmentation accuracy, yielding a standard deviation of the DSC score of 0.01. The proposed method also obtains the lowest ASSD and MSD scores on average (2.6 mm and 31.5 mm, respectively). Moreover, for the proposed method, a median LSF error of 0.14% is obtained, which is a statically significant improvement to the state-of-the-art-method (p=0.004), and is much smaller than the median error in LSF manual determination by the medical experts using 2D planar image (1.74% and p<0.001). Conclusions: A method for LSF quantification using 3D SPECT/CT images based on CNNs and non-rigid registration was proposed, evaluated and compared to state-of-the-art techniques. The proposed method can quantitatively determine the LSF with high accuracy and has the potential to be applied in clinical practice.}\n}\n\n
\n
\n\n\n
\n Purpose: Selective internal radiation therapy (SIRT) has been proven to be an effective treatment for hepatocellular carcinoma (HCC) patients. In clinical practice, the treatment planning for SIRT using 90Y microspheres requires estimation of the liver-lung shunt fraction (LSF) to avoid radiation pneumonitis. Currently, the manual segmentation method to draw a region of interest (ROI) of the liver and lung in 2D planar imaging of 99mTc-MAA and 3D SPECT/CT images is inconvenient, time-consuming and observer-dependent. In this study, we propose and evaluate a nearly automatic method for LSF quantification using 3D SPECT/CT images, offering improved performance compared with the current manual segmentation method. Methods: We retrospectively acquired 3D SPECT with non-contrast-enhanced CT images (nCECT) of 60 HCC patients from a SPECT/CT scanning machine, along with the corresponding diagnostic contrast-enhanced CT images (CECT). Our approach for LSF quantification is to use CNN-based methods for liver and lung segmentations in the nCECT image. We first apply 3D ResUnet to coarsely segment the liver. If the liver segmentation contains a large error, we dilate the coarse liver segmentation into the liver mask as a ROI in the nCECT image. Subsequently, non-rigid registration is applied to deform the liver in the CECT image to fit that obtained in the nCECT image. The final liver segmentation is obtained by segmenting the liver in the deformed CECT image using nnU-Net. In addition, the lung segmentations are obtained using 2D ResUnet. Finally, LSF quantitation is performed based on the number of counts in the SPECT image inside the segmentations. Evaluations and Results: To evaluate the liver segmentation accuracy, we used Dice similarity coefficient (DSC), asymmetric surface distance (ASSD), and max surface distance (MSD) and compared the proposed method to five well-known CNN-based methods for liver segmentation. Furthermore, the LSF error obtained by the proposed method was compared to a state-of-the-art method, modified Deepmedic, and the LSF quantifications obtained by manual segmentation. The results show that the proposed method achieved a DSC score for the liver segmentation that is comparable to other state-of-the-art methods, with an average of 0.93, and the highest consistency in segmentation accuracy, yielding a standard deviation of the DSC score of 0.01. The proposed method also obtains the lowest ASSD and MSD scores on average (2.6 mm and 31.5 mm, respectively). Moreover, for the proposed method, a median LSF error of 0.14% is obtained, which is a statically significant improvement to the state-of-the-art-method (p=0.004), and is much smaller than the median error in LSF manual determination by the medical experts using 2D planar image (1.74% and p<0.001). Conclusions: A method for LSF quantification using 3D SPECT/CT images based on CNNs and non-rigid registration was proposed, evaluated and compared to state-of-the-art techniques. The proposed method can quantitatively determine the LSF with high accuracy and has the potential to be applied in clinical practice.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2022\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Efficient Type and Polarity Classification of Chromosome Images using CNNs: a Primary Evaluation on Multiple Datasets.\n \n \n \n\n\n \n Anh, L. Q.; Thanh, V. D.; Son, N. H. H.; Phuong, D. T. K.; Anh, L. T. L.; Ram, D. T.; Minh, N. T. B.; Tung, T. H.; Thinh, N. H.; Ha, L. V.; and Ha, L. M.\n\n\n \n\n\n\n In 2022 IEEE Ninth International Conference on Communications and Electronics (ICCE), pages 400-405, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@INPROCEEDINGS{9852034,\nauthor={Anh, Le Quoc and Thanh, Vu Duy and Son, Nguyen Huu Hoang and Phuong, Doan Thi Kim and Anh, Luong Thi Lan and Ram, Do Thi and Minh, Nguyen Thanh Binh and Tung, Tran Hoang and Thinh, Nguyen Hong and Ha, Le Vu and Ha, Luu Manh},\nbooktitle={2022 IEEE Ninth International Conference on Communications and Electronics (ICCE)}, \ntitle={Efficient Type and Polarity Classification of Chromosome Images using CNNs: a Primary Evaluation on Multiple Datasets}, \nyear={2022},\nvolume={},\nnumber={},\npages={400-405},\nkeywords={Training;Electric potential;Hospitals;Manuals;Genetics;Convolutional neural networks;Task analysis;Karyotyping;chromosome;classification;deep learning;EfficientNetV2},\ndoi={10.1109/ICCE55644.2022.9852034}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Needle Localization and Segmentation for Radiofrequency Ablation of Liver Tumors under CT Image Guidance.\n \n \n \n\n\n \n Anh, L. Q.; Ha, L. M.; Van Walsum, T.; Moelker, A.; Hang, D. V.; Phuong, P. C.; and Thanh, V. D.\n\n\n \n\n\n\n In 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pages 2015-2021, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@INPROCEEDINGS{9980132,\nauthor={Anh, Le Quoc and Ha, Luu Manh and Van Walsum, Theo and Moelker, Adriaan and Hang, Dao Viet and Phuong, Pham Cam and Thanh, Vu Duy},\nbooktitle={2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)}, \ntitle={Needle Localization and Segmentation for Radiofrequency Ablation of Liver Tumors under CT Image Guidance}, \nyear={2022},\nvolume={},\nnumber={},\npages={2015-2021},\nkeywords={Radio frequency;Shafts;Image segmentation;Visualization;Three-dimensional displays;Computed tomography;Liver;Liver tumors;RFA;needle segmentation;CT guidance;projections;CNN},\ndoi={10.23919/APSIPAASC55919.2022.9980132}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Multi-resolution Coarse-to-fine Registration Approach for Liver Computed Tomography Image Analysis.\n \n \n \n\n\n \n Loc Pham, X.; Anh Le, Q.; Trinh Chu, D.; and Ha Luu, M.\n\n\n \n\n\n\n In 2022 26th International Computer Science and Engineering Conference (ICSEC), pages 307-312, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@INPROCEEDINGS{10049333,\nauthor={Loc Pham, Xuan and Anh Le, Quoc and Trinh Chu, Duc and Ha Luu, Manh},\nbooktitle={2022 26th International Computer Science and Engineering Conference (ICSEC)}, \ntitle={Multi-resolution Coarse-to-fine Registration Approach for Liver Computed Tomography Image Analysis}, \nyear={2022},\nvolume={},\nnumber={},\npages={307-312},\nkeywords={Measurement;Liver cancer;Image segmentation;Image analysis;Deformation;Shape;Computed tomography;unsuperivsed learning;coarse-to-fine registration;liver computed tomography image;large deformation},\ndoi={10.1109/ICSEC56337.2022.10049333}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2021\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Short time cardio-vascular pulses estimation for dengue fever screening via continuous-wave Doppler radar using empirical mode decomposition and continuous wavelet transform.\n \n \n \n \n\n\n \n Chinh, N. D.; Ha, L. M.; Sun, G.; Anh, L. Q.; Huong, P. V.; Vu, T. A.; Hieu, T. T.; Tan, T. D.; Trung, N. V.; Ishibashi, K.; and Trung, N. L.\n\n\n \n\n\n\n Biomedical Signal Processing and Control, 65: 102361. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"ShortPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{CHINH2021102361,\ntitle = {Short time cardio-vascular pulses estimation for dengue fever screening via continuous-wave Doppler radar using empirical mode decomposition and continuous wavelet transform},\njournal = {Biomedical Signal Processing and Control},\nvolume = {65},\npages = {102361},\nyear = {2021},\nissn = {1746-8094},\ndoi = {https://doi.org/10.1016/j.bspc.2020.102361},\nurl = {https://www.sciencedirect.com/science/article/pii/S1746809420304699},\nauthor = {Nguyen Dinh Chinh and Luu Manh Ha and Guanghao Sun and Le Quoc Anh and Pham Viet Huong and Tran Anh Vu and Tran Trong Hieu and Tran Duc Tan and Nguyen Vu Trung and Koichiro Ishibashi and Nguyen Linh Trung},\nkeywords = {Continuous-wave Doppler radar, Dengue fever screening, Short time, Heart rate, EMD, CWT},\nabstract = {Contactless measurement of cardio-vascular pulse acts an essential role in clinical medical sectors. Estimating cardio-vascular pulses based on continuous-wave (CW) Doppler radar in limited time while maintaining the accuracy is a challenging task. In this paper, we propose a signal processing method that combines empirical mode decomposition (EMD) and continuous wavelet transform (CWT) for a short time estimation of heart rate (HR) and inter-beat-interval from radar signals. We evaluate performance of the proposed method using 85 patients with dengue fever and 40 healthy subjects. Subsequently, the estimated contactless HR is compared to that of a commercial contact-type medical device. The result shows that the HR can be estimated within a period of 5 s with an accuracy of 96.2 ± 2.5%. The patients with dengue fever show an elevated HR and a decreased standard deviation of heartbeat interval (SDHI). Finally, linear discriminant analysis (LDA) is utilized with two parameters HR and SDHI to classify the dengue patients, achieving the sensitivity and specificity values of 86.3% and 86.9%, respectively.}\n}\n\n
\n
\n\n\n
\n Contactless measurement of cardio-vascular pulse acts an essential role in clinical medical sectors. Estimating cardio-vascular pulses based on continuous-wave (CW) Doppler radar in limited time while maintaining the accuracy is a challenging task. In this paper, we propose a signal processing method that combines empirical mode decomposition (EMD) and continuous wavelet transform (CWT) for a short time estimation of heart rate (HR) and inter-beat-interval from radar signals. We evaluate performance of the proposed method using 85 patients with dengue fever and 40 healthy subjects. Subsequently, the estimated contactless HR is compared to that of a commercial contact-type medical device. The result shows that the HR can be estimated within a period of 5 s with an accuracy of 96.2 ± 2.5%. The patients with dengue fever show an elevated HR and a decreased standard deviation of heartbeat interval (SDHI). Finally, linear discriminant analysis (LDA) is utilized with two parameters HR and SDHI to classify the dengue patients, achieving the sensitivity and specificity values of 86.3% and 86.9%, respectively.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);