SenseCare: a Research Platform for Medical Image Informatics and Interactive 3D Visualization

Overview

Journal Front Radiol

Specialty Radiology

Date 2024 Dec 6

PMID 39639965

Authors

Guotai Wang

Qi Duan

Tian Shen

Shaoting Zhang

Affiliations

Soon will be listed here.

Abstract

Introduction: Clinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. However, existing research platforms for medical image informatics have limited support for Artificial Intelligence (AI) algorithms and clinical applications.

Methods: To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios. It has several appealing functions and features such as advanced 3D visualization, concurrent and efficient web-based access, fast data synchronization and high data security, multi-center deployment, support for collaborative research, etc.

Results And Discussion: SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. It also facilitates the data annotation and model training processes, which makes it easier for clinical researchers to develop and deploy customized AI models. In addition, it is clinic-oriented and supports various clinical applications such as diagnosis and surgical planning for lung cancer, liver tumor, coronary artery disease, etc. By simplifying AI-based medical image analysis, SenseCare has a potential to promote clinical research in a wide range of disease diagnosis and treatment applications.

References

Klein S, Staring M, Murphy K, Viergever M, Pluim J . elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2009; 29(1):196-205. DOI: 10.1109/TMI.2009.2035616. View

Clarkson M, Zombori G, Thompson S, Totz J, Song Y, Espak M . The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging. Int J Comput Assist Radiol Surg. 2014; 10(3):301-16. PMC: 4338364. DOI: 10.1007/s11548-014-1124-7. View

Qu H, Wu P, Huang Q, Yi J, Yan Z, Li K . Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images. IEEE Trans Med Imaging. 2020; 39(11):3655-3666. DOI: 10.1109/TMI.2020.3002244. View

Shen D, Wu G, Suk H . Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng. 2017; 19:221-248. PMC: 5479722. DOI: 10.1146/annurev-bioeng-071516-044442. View

Rivest-Henault D, Dowson N, Greer P, Fripp J, Dowling J . Robust inverse-consistent affine CT-MR registration in MRI-assisted and MRI-alone prostate radiation therapy. Med Image Anal. 2015; 23(1):56-69. DOI: 10.1016/j.media.2015.04.014. View

Zhang X, Xing F, Su H, Yang L, Zhang S . High-throughput histopathological image analysis via robust cell segmentation and hashing. Med Image Anal. 2015; 26(1):306-15. PMC: 4679540. DOI: 10.1016/j.media.2015.10.005. View

Oulefki A, Agaian S, Trongtirakul T, Benbelkacem S, Aouam D, Zenati-Henda N . Virtual Reality visualization for computerized COVID-19 lesion segmentation and interpretation. Biomed Signal Process Control. 2021; 73:103371. PMC: 8610934. DOI: 10.1016/j.bspc.2021.103371. View

Huang S, Kothari T, Banerjee I, Chute C, Ball R, Borus N . PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. NPJ Digit Med. 2020; 3:61. PMC: 7181770. DOI: 10.1038/s41746-020-0266-y. View

Song Z, Yu C, Zou S, Wang W, Huang Y, Ding X . Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists. BMJ Open. 2020; 10(9):e036423. PMC: 7485250. DOI: 10.1136/bmjopen-2019-036423. View

10.

Zhang S, Metaxas D . On the challenges and perspectives of foundation models for medical image analysis. Med Image Anal. 2023; 91:102996. DOI: 10.1016/j.media.2023.102996. View

11.

Wang G, Liu X, Li C, Xu Z, Ruan J, Zhu H . A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images. IEEE Trans Med Imaging. 2020; 39(8):2653-2663. PMC: 8544954. DOI: 10.1109/TMI.2020.3000314. View

12.

Wu Y, Qi S, Wang M, Zhao S, Pang H, Xu J . Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images. Med Biol Eng Comput. 2023; 61(10):2649-2663. DOI: 10.1007/s11517-023-02872-5. View

13.

Luo X, Song T, Wang G, Chen J, Chen Y, Li K . SCPM-Net: An anchor-free 3D lung nodule detection network using sphere representation and center points matching. Med Image Anal. 2021; 75:102287. DOI: 10.1016/j.media.2021.102287. View

14.

Xie W, Jacobs C, Charbonnier J, van Ginneken B . Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans. IEEE Trans Med Imaging. 2020; 39(8):2664-2675. PMC: 7393217. DOI: 10.1109/TMI.2020.2995108. View

15.

Chen Y, Gao Y, Li K, Zhao L, Zhao J . Vertebrae Identification and Localization Utilizing Fully Convolutional Networks and a Hidden Markov Model. IEEE Trans Med Imaging. 2019; 39(2):387-399. DOI: 10.1109/TMI.2019.2927289. View

16.

Modat M, Ridgway G, Taylor Z, Lehmann M, Barnes J, Hawkes D . Fast free-form deformation using graphics processing units. Comput Methods Programs Biomed. 2009; 98(3):278-84. DOI: 10.1016/j.cmpb.2009.09.002. View

17.

Wang W, Xia Q, Hu Z, Yan Z, Li Z, Wu Y . Few-Shot Learning by a Cascaded Framework With Shape-Constrained Pseudo Label Assessment for Whole Heart Segmentation. IEEE Trans Med Imaging. 2021; 40(10):2629-2641. DOI: 10.1109/TMI.2021.3053008. View

18.

Dai X, Huang L, Qian Y, Xia S, Chong W, Liu J . Deep learning for automated cerebral aneurysm detection on computed tomography images. Int J Comput Assist Radiol Surg. 2020; 15(4):715-723. DOI: 10.1007/s11548-020-02121-2. View

19.

Wang W, Xia Q, Yan Z, Hu Z, Chen Y, Zheng W . AVDNet: Joint coronary artery and vein segmentation with topological consistency. Med Image Anal. 2023; 91:102999. DOI: 10.1016/j.media.2023.102999. View

20.

Hong J, Tzeng Y, Yin W, Wu K, Hsu H, Lu C . Automated coronary artery calcium scoring using nested U-Net and focal loss. Comput Struct Biotechnol J. 2022; 20:1681-1690. PMC: 9010683. DOI: 10.1016/j.csbj.2022.03.025. View