Generic placeholder image

Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Robust Engineering-based Unified Biomedical Imaging Framework for Liver Tumor Segmentation

Author(s): Vuong Pham, Hai Nguyen, Bao Pham, Thien Nguyen and Hien Nguyen*

Volume 19, Issue 1, 2023

Published on: 03 February, 2022

Page: [37 - 45] Pages: 9

DOI: 10.2174/1573405617666210804151024


Background: Computer vision in general and semantic segmentation has experienced many achievements in recent years. Consequently, the emergence of medical imaging has provided new opportunities for conducting artificial intelligence research. Since cancer is the second-leading cause of death in the world, early-stage diagnosis is an essential process that directly slows down the development speed of cancer.

Methods: Deep neural network-based methods are anticipated to reduce diagnosis time for pathologists.

Results: In this research paper, an approach to liver tumor identification based on two types of medical images has been presented: computed tomography scans and whole-slide. It is constructed based on the improvement of U-Net and GLNet architectures. It also includes sub-modules that are combined with segmentation models to boost up the overall performance during inference phases.

Conclusion: Based on the experimental results, the proposed unified framework has been emerging to be used in the production environment.

Keywords: Tumor segmentation, radiology, histopathology, neural networks, framework, deep learning.

Graphical Abstract
WHO Global cancer fact sheets in 2018 WHO. 2018.
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. 234-41.
Milletari F, Navab N, Ahmadi S-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. 2016 4th International Conference on 3D vision (3DV). 565-71.
Li X, Chen H, Qi X, Dou Q, Fu CW, Heng P-A. H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Trans Med Imaging 2018; 37(12): 2663-74.
[] [PMID: 29994201]
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. Unet++: A nested unet architecture for medical image segmentation.Deep learning in medical image analysis and multimodal learning for clinical decision support DLMIA 2018, ML-CDS. 2018; pp. 3-11.
Weng Y, Zhou T, Li Y, Qiu X. Nas-unet: Neural architecture search for medical image segmentation. IEEE Access 2019; (7): 44247-57.
Nguyen H, Huynh T, Hoang S, et al. Language-oriented sentiment analysis based on the grammar structure and improved Self-attention network. Proceedings of 15th International Conference on Evaluation of Novel Approaches to Software Engineering.
Phan T, Pham V, et al. Ontology-based resume searching system for job applicants in information technology. Proceedings of 34th International Conference on. In publishing.
Khan A, Narejo G. Analysis of abdominal computed tomography images for automatic liver cancer diagnosis using image processing algorithm. Curr Med Imaging 2019; 15(10): 972-82.
Wu W, Wu S, Zhou Z, Zhang R, Zhang Y. 3D liver tumor segmentation in CT images using improved fuzzy C-means and graph cuts. BioMed Res Int 2017; 2017: 5207685.
[] [PMID: 29090220]
Haralick RM, Shapiro LG. Image segmentation techniques. Computer vision, graphics, and image processing 1985; 29(1): 100-32.
Yuheng S, Hao Y. Image segmentation algorithms overview. 2017 Asia Modelling Symposium (AMS). 103-97.
Nguyen B, Trinh M, Phan T, Nguyen H. An efficient real-time emotion detection using camera and facial landmarks. Proceedings of The Seventh International Conference on Information Science and Technology.
Huynh A, Nguyen BT, Nguyen HT, Vu S, Nguyen HD. A method of deep reinforcement learning for simulated autonomous vehicle control. Proceedings of 16th International Conference on Evaluation of Novel Approaches to Software Engineering.
Srimathi S, Yamuna G, Nanmaran R. An efficient cancer classification model for CT/MRI/PET fused images. Curr Med Imaging 2021; 17(3): 319-30.
Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical analysis. IEEE Access 2018; (6): 9375-89.
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition.
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3d unet: learning dense volumetric segmentation from sparse annotation. International conference on medical image computing and computer-assisted intervention.
Chen H, Dou Q, Yu L, Qin J, Heng P-A. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. Neuroimage 2018; 170(170): 446-55.
[] [PMID: 28445774]
Meng L, Tian Y, Bu S. Liver tumor segmentation based on 3D convolutional neural network with dual scale. J Appl Clin Med Phys 2020; 21(1): 144-57.
Nguyen H, Tran P, Pham V, Nguyen H. Design a learning model of mobile vision to detect diabetic retinopathy based on the improvement of mobilenetv2.Int J Digital Enterprise Technology IJDET. 2021.
Sandler M, Howard A, Zhu M, Zhmoginov A, L C. Mobilenetv2: Inverted residuals and linear bottlenecks. IEEE International Conferene on Computer Vision and Pattern Recognition.
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42(42): 60-88.
[] [PMID: 28778026]
Pinchaud N, Hedlund M. Camelyon17 grand challenge. 2017.
Kim YJ, Jang H, Lee K, et al. PAIP 2019: Liver cancer segmentation challenge. Med Image Anal 2021; 67(67): 101854.
[] [PMID: 33091742]
Aresta G, Araújo T, Kwok S, et al. BACH: Grand challenge on breast cancer histology images. Med Image Anal 2019; 56(56): 122-39.
[] [PMID: 31226662]
Li J, Yang S, Huang X, Da Q, et al. Signet ring cell detection with a semi-supervised learning framework. International Conference on Information Processing in Medical Imaging.
Sirinukunwattana K, Raza S E A, et al. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 2019; 35(5): 1196-206.
Schlemper J, Oktay O, Schaap M, et al. 2019.
[] [PMID: 30802813]
Chen W, Jiang Z, Wang Z, Cui K, Qian X. Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images. IEEE International Conference of Computer Vision and Pattern Recognition (CVPR).
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations (ICLR).
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE International conference on Computer Vision and Pattern Recognition (CVPR).
Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception-ResNet and the impact of residual connections on learning. 31st AAAI Conference on Artificial Intelligence (AAAI). 4278-84.
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS).
Schlemper J, Oktay O, Schaap M, et al. Attention gated networks: Learning to leverage salient regions in medical images. Med Image Anal 2019; 53(53): 197-207.
[] [PMID: 30802813]
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS. IEEE Trans Pattern Anal Mach Intell 2017; 40(4): 834-48.
Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. IEEE International conference on Computer Vision and Pattern Recognition (CVPR).
Demir I, Koperski K, Lindenbaum D, et al. Deepglobe 2018: A challenge to parse the earth through satellite images. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.
Codella NC, Gutman D, Celebi ME, et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC). IEEE 15th International Symposium on Biomedical Imaging. 168-72.
Maggiori E, Tarabalka Y, Charpiat G, Alliez P. Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark. IEEE International Geoscience and Remote Sensing Symposium. 3226-9.
Bilic P, Christ P F, Vorontsov E. The liver tumor segmentation benchmark (LiTS). arXiv preprint arXiv:190104056.
Lin T, Goyal P, Girshick R, He , Doll'ar . Focal loss for dense object detection. IEEE International Conference on Computer Vision (ICCV) 2017.
Do N, Nguyen H, Selamat A. Knowledge-based model of expert systems using rela-model. Int J Softw Eng Knowl Eng 2018; 28(8): 1047-90.
Nguyen HD, Tran DA, Do HP, Pham V. Design an intelligent system to automatically tutor the method for solving problems. Int J Integr Eng 2020; 12(7): 211-23.

© 2023 Bentham Science Publishers | Privacy Policy