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Hettihewa K, Kobchaisawat T, Tanpowpong N, Chalidabhongse TH. MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging. Sci Rep 2023; 13:20098. [PMID: 37973987 PMCID: PMC10654423 DOI: 10.1038/s41598-023-46580-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023] Open
Abstract
Automatic liver tumor segmentation is a paramount important application for liver tumor diagnosis and treatment planning. However, it has become a highly challenging task due to the heterogeneity of the tumor shape and intensity variation. Automatic liver tumor segmentation is capable to establish the diagnostic standard to provide relevant radiological information to all levels of expertise. Recently, deep convolutional neural networks have demonstrated superiority in feature extraction and learning in medical image segmentation. However, multi-layer dense feature stacks make the model quite inconsistent in imitating visual attention and awareness of radiological expertise for tumor recognition and segmentation task. To bridge that visual attention capability, attention mechanisms have developed for better feature selection. In this paper, we propose a novel network named Multi Attention Network (MANet) as a fusion of attention mechanisms to learn highlighting important features while suppressing irrelevant features for the tumor segmentation task. The proposed deep learning network has followed U-Net as the basic architecture. Moreover, residual mechanism is implemented in the encoder. Convolutional block attention module has split into channel attention and spatial attention modules to implement in encoder and decoder of the proposed architecture. The attention mechanism in Attention U-Net is integrated to extract low-level features to combine with high-level ones. The developed deep learning architecture is trained and evaluated on the publicly available MICCAI 2017 Liver Tumor Segmentation dataset and 3DIRCADb dataset under various evaluation metrics. MANet demonstrated promising results compared to state-of-the-art methods with comparatively small parameter overhead.
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Affiliation(s)
- Kasun Hettihewa
- Perceptual Intelligent Computing Laboratory, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
| | | | - Natthaporn Tanpowpong
- Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Thanarat H Chalidabhongse
- Perceptual Intelligent Computing Laboratory, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
- Applied Digital Technology in Medicine (ATM) Research Group, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
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Buttongkum D, Tangpornprasert P, Virulsri C, Numkarunarunrote N, Amarase C, Kobchaisawat T, Chalidabhongse T. 3D reconstruction of proximal femoral fracture from biplanar radiographs with fractural representative learning. Sci Rep 2023; 13:455. [PMID: 36624184 PMCID: PMC9829664 DOI: 10.1038/s41598-023-27607-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
A femoral fracture is a severe injury occurring in traumatic and pathologic causes. Diagnosis and Preoperative planning are indispensable procedures relying on preoperative radiographs such as X-ray and CT images. Nevertheless, CT imaging has a higher cost, radiation dose, and longer acquisition time than X-ray imaging. Thus, the fracture 3D reconstruction from X-ray images had been needed and remains a challenging problem, as well as a lack of dataset. This paper proposes a 3D proximal femoral fracture reconstruction from biplanar radiographs to improve the 3D visualization of bone fragments during preoperative planning. A novel Fracture Reconstruction Network (FracReconNet) is proposed to retrieve the femoral bone shape with fracture details, including the 3D Reconstruction Network (3DReconNet), novel Auxiliary class (AC), and Fractural augmentation (FA). The 3D reconstruction network applies a deep learning-based, fully Convolutional Network with Feature Pyramid Network architecture. Specifically, the auxiliary class is proposed, which refers to fracture representation. It encourages network learning to reconstruct the fracture. Since the samples are scarce to acquire, the fractural augmentation is invented to enlarge the fracture training samples and improve reconstruction accuracy. The evaluation of FracReconNet achieved a mIoU of 0.851 and mASSD of 0.906 mm. The proposed FracReconNet's results show fracture detail similar to the real fracture, while the 3DReconNet cannot offer.
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Affiliation(s)
- Danupong Buttongkum
- grid.7922.e0000 0001 0244 7875Center of Excellence for Prosthetic and Orthopedic Implant, Chulalongkorn University, Bangkok, 10330 Thailand ,grid.7922.e0000 0001 0244 7875Biomedical Engineering Research Center, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330 Thailand
| | - Pairat Tangpornprasert
- Center of Excellence for Prosthetic and Orthopedic Implant, Chulalongkorn University, Bangkok, 10330, Thailand. .,Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. .,Biomedical Engineering Research Center, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Chanyaphan Virulsri
- grid.7922.e0000 0001 0244 7875Center of Excellence for Prosthetic and Orthopedic Implant, Chulalongkorn University, Bangkok, 10330 Thailand ,grid.7922.e0000 0001 0244 7875Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330 Thailand ,grid.7922.e0000 0001 0244 7875Biomedical Engineering Research Center, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330 Thailand
| | - Numphung Numkarunarunrote
- grid.7922.e0000 0001 0244 7875Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330 Thailand
| | - Chavarin Amarase
- grid.7922.e0000 0001 0244 7875Hip Fracture Research Unit, Department of Orthopaedics, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330 Thailand
| | - Thananop Kobchaisawat
- grid.7922.e0000 0001 0244 7875Perceptual Intelligent Computing Lab, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330 Thailand
| | - Thanarat Chalidabhongse
- grid.7922.e0000 0001 0244 7875Perceptual Intelligent Computing Lab, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330 Thailand ,grid.7922.e0000 0001 0244 7875Applied Digital Technology in Medicine Research Group, Chulalongkorn University, Bangkok, 10330 Thailand
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Palasuwan D, Chalidabhongse TH, Chancharoen R, Palasuwan A, Kobchaisawat T, Phanomchoeng G. G6PD diaxBox: Digital image-based quantification of G6PD deficiency. Talanta 2021; 233:122538. [PMID: 34215041 DOI: 10.1016/j.talanta.2021.122538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 11/26/2022]
Abstract
Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common enzymopathy in humans. More than 400 million people worldwide are affected by this genetic condition. Testing for G6PD deficiency before drug administration is essential for patient safety. Rapidly ascertaining the G6PD status of a person is desirable for proper treatment. The device described in this study, the G6PD diaxBOX, was developed to quantify G6PD deficiency using paper-based analytical devices (PADs) and a colorimetric assay. The G6PD diaxBOX is a straightforward, affordable, portable, and instrument-free analytical system. The major components of the G6PD diaxBox are a banknote-checking UV fluorescent lamp and camera that are easy to access and analysis software. When NADPH is generated, it absorbs at UV 340 nm and emits colored light that is detected with the camera. The determined Pearson's coefficient shows that the color intensity measured from the G6PD diaxBOX correlated with G6PD activity level. Also, a Bland-Altman analysis indicated that more than 95% of the measurement error was in the upper and lower boundaries (±2 SD) and the error from the severe and moderate deficiency group was less than ± 1 SD. Therefore, the error from G6PD diaxBOX was within the limit boundary and the overall accuracy was more than 80%. The G6PD diaxBOX facilitates the effective and efficient quantification of G6PD deficiency and as such represents a clinically well-suited, rapid point-of-care test.
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Affiliation(s)
- Duangdao Palasuwan
- Oxidation in Red Cell Disorders Research Unit, Department of Clinical Microscopy, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Thanarat H Chalidabhongse
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand; Research Group on Applied Digital Technology in Medicine (ATM), Chulalongkorn University, Bangkok, 10330, Thailand
| | - Ratchatin Chancharoen
- Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Attakorn Palasuwan
- Oxidation in Red Cell Disorders Research Unit, Department of Clinical Microscopy, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Thananop Kobchaisawat
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand; AI Engineering Center, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Gridsada Phanomchoeng
- Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand; Applied Medical Virology Research Unit, Chulalongkorn University, Bangkok, 10300, Thailand.
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