Innovative Multimodal Approaches in Image-Based Analysis of Adipose Tissue Cells
DOI:
https://doi.org/10.26555/jiteki.v10i4.30241Abstract
This study addresses the limitations of traditional single-modality imaging techniques, such as optical microscopy, in effectively analyzing adipose tissue cells. A novel multimodal approach is introduced to overcome these challenges, combining MRI, CT, and microscopy to provide a more comprehensive and precise dataset. The system automates image processing, utilizing advanced segmentation methods to detect adipose cells more accurately while calculating cell dimensions and total image area. The results indicate that the maximum observed cell diameter reaches 10,466.64 µm, with a minimum diameter of 0.40 µm and an average diameter of 2,398.31 µm across the sample images. All measurements achieved 0% mean square error (MSE), highlighting the precision of the method. Comparative analysis reveals significant improvements in accuracy for both cell detection and quantification, outperforming conventional methods. Graphical representations further validate the reliability of this multimodal approach, demonstrating its capacity to capture intricate details of cellular structures. This innovative method holds considerable promise for enhancing medical diagnostics, particularly in metabolic disorders like obesity and diabetes, where adipose tissue plays a pivotal role. Integrating multiple imaging modalities offers a powerful tool for more informed clinical decisions, potentially leading to improved patient outcomes.
References
[1] A. Sakers, M. K. De Siqueira, P. Seale, and C. J. Villanueva, “Adipose-tissue plasticity in health and disease,” Cell, vol. 185, no. 3, pp. 419-446, 2022, https://doi.org/10.1016/j.cell.2021.12.016.
[2] M. K. Debari and R. D. Abbott, “Adipose tissue fibrosis: Mechanisms, models, and importance,” International journal of molecular sciences, vol. 21, no. 17, pp. 6030, 2020, https://doi.org/10.3390/ijms21176030.
[3] G. Martinez-Santibañez, K. W. Cho, and C. N. Lumeng, “Imaging white adipose tissue with confocal microscopy,” in Methods in Enzymology, vol. 5372014, pp. 17–30, 2014, https://doi.org/10.1016/B978-0-12-411619-1.00002-1.
[4] K. A. Britton, J. M. Massaro, J. M. Murabito, B. E. Kreger, U. Hoffmann, and C. S. Fox, “Body fat distribution, incident cardiovascular disease, cancer, and all-cause mortality,” J Am Coll Cardiol, vol. 62, no. 10, pp. 921–925, Sep. 2013, https://doi.org/10.1016/j.jacc.2013.06.027.
[5] K. Nikiforaki and K. Marias, “MRI Methods to Visualize and Quantify Adipose Tissue in Health and Disease,” Multidisciplinary Digital Publishing Institute (MDPI), p. 3179, 2023, https://doi.org/10.3390/biomedicines11123179.
[6] L. Dilworth, A. Facey, F. Omoruyi, “Diabetes mellitus and its metabolic complications: the role of adipose tissues,” International journal of molecular sciences, vol. 22, no. 14, p. 7644, 2021, https://doi.org/10.3390/ijms22147644.
[7] P. Lei, J. Li, J. Yi, and W. Chen, “Adipose Tissue Segmentation after Lung Slice Localization in Chest CT Images Based on ConvBiGRU and Multi-Module UNet,” Biomedicines, vol. 12, no. 5, May 2024, https://doi.org/10.3390/biomedicines12051061.
[8] K. X. Tang et al., “An enhanced deep learning method for the quantification of epicardial adipose tissue,” Sci Rep, vol. 14, no. 1, p. 24947, Dec. 2024, https://doi.org/10.1038/s41598-024-75659-9.
[9] S. Gokce Kafali et al., “3D Neural Networks for Visceral and Subcutaneous Adipose Tissue Segmentation using Volumetric Multi-Contrast MRI,” In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) , pp. 3933-3937, 2021, https://doi.org/10.1109/EMBC46164.2021.9630110.
[10] V. M. Lauschke and C. E. Hagberg, “Next-generation human adipose tissue culture methods,” Current Opinion in Genetics & Development, vol, 80, pp. 102057., 2023, https://doi.org/10.1016/j.gde.2023.102057.
[11] C. D. Mccormick et al., “Subcutaneous adipose tissue imaging of human obesity reveals two types of adipocyte membranes: Insulinresponsive and-nonresponsive,” Journal of Biological Chemistry, vol. 293, no. 37, pp. 14249–14259, Sep. 2018, https://doi.org/10.1074/jbc.RA118.003751.
[12] Y. Y. Mo et al., “Adipose Tissue Plasticity: A Comprehensive Definition and Multidimensional Insight,” Biomolecules, vol, 14, no. 10, p. 1223, 2024, https://doi.org/10.3390/biom14101223.
[13] J. Yang et al., “Molecular imaging of brown adipose tissue mass,” International Journal of Molecular Sciences, vol. 22, no. 17, pp. 9436. 2021, https://doi.org/10.3390/ijms22179436.
[14] L. Saba et al., “Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application,” Ann Transl Med, vol. 9, no. 14, pp. 1206–1206, Jul. 2021, https://doi.org/10.21037/atm-20-7676.
[15] W. Tang, F. He, Y. Liu, and Y. Duan, “MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer,” IEEE Transactions on Image Processing, vol. 31, pp. 5134–5149, 2022, https://doi.org/10.1109/TIP.2022.3193288.
[16] X. Hui et al., “Multimodal Imaging Approach to Monitor Browning of Adipose Tissue In Vivo,” Journal of lipid research,vol. 59, no. 6, pp. 1071-1078, 2018 [Online]. Available: www.jlr.org.
[17] H. N. Dao, T. Nguyen, C. Mugisha, and I. Paik, “A Multimodal Transfer Learning Approach Using PubMedCLIP for Medical Image Classification,” IEEE Access, vol. 12, pp. 75496–75507, 2024, https://doi.org/10.1109/ACCESS.2024.3401777.
[18] X. H. D. Chan et al., “Multimodal imaging approach to monitor browning of adipose tissue in vivo,” J Lipid Res, vol. 59, no. 6, pp. 1071–1078, 2018, https://doi.org/10.1194/jlr.D083410.
[19] X. Yi, Y. He, S. Gao, and M. Li, “A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, p. 103000, 2024, https://doi.org/10.1016/j.dsx.2024.103000.
[20] S. Koitka, L. Kroll, E. Malamutmann, A. Oezcelik, and F. Nensa, “Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks,” Eur Radiol, vol. 31, no. 4, pp. 1795–1804, Apr. 2021, https://doi.org/10.1007/s00330-020-07147-3.
[21] A. Lin et al., “Deep Learning Analysis of the Adipose Tissue and the Prediction of Prognosis in Colorectal Cancer,” Front Nutr, vol. 9, May 2022, https://doi.org/10.3389/fnut.2022.869263.
[22] P. Daudé et al., “Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging,” Diagnostics, vol. 12, no. 1, p. 126, 2022, https://doi.org/10.3390/diagnostics.
[23] A. Rawshani et al., “Adipose tissue morphology, imaging and metabolomics predicting cardiometabolic risk and family history of type 2 diabetes in non-obese men,” Sci Rep, vol. 10, no. 1, Dec. 2020, https://doi.org/10.1038/s41598-020-66199-z.
[24] X. Jin et al., “Pathophysiology of obesity and its associated diseases,” Acta Pharmaceutica Sinica B, vol, 13, no. 6, pp. 2403-2424, 2023, https://doi.org/10.1016/j.apsb.2023.01.012.
[25] S. Hussein et al., “Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans,” IEEE Trans Med Imaging, vol. 36, no. 3, pp. 734–744, Mar. 2017, https://doi.org/10.1109/TMI.2016.2636188.
[26] S. Chen, D. An, C. Feng, Z. Bian, and L. M. Wu, “Segmentation of Pericardial Adipose Tissue in CMR Images: A Benchmark Dataset MRPEAT and a Triple-Stage Network 3SUnet,” IEEE Trans Med Imaging, vol. 42, no. 8, pp. 2386–2399, Aug. 2023, https://doi.org/10.1109/TMI.2023.3251368.
[27] S. Gokce Kafali et al., “3D Neural Networks for Visceral and Subcutaneous Adipose Tissue Segmentation using Volumetric Multi-Contrast MRI,” In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 3933-3937, 2021, https://doi.org/10.1109/EMBC46164.2021.9630110.
[28] G. Aringhieri et al., “Convolutional Neural Network-Based Automated Segmentation of Skeletal Muscle and Subcutaneous Adipose Tissue on Thigh MRI in Muscular Dystrophy Patients,” J Funct Morphol Kinesiol, vol. 9, no. 3, p. 123, Jul. 2024, https://doi.org/10.3390/jfmk9030123.
[29] T. Küstner et al., “Fully automated and standardized segmentation of adipose tissue compartments via deep learning in 3D whole-body MRI of epidemiologic cohort studies,” Radiol Artif Intell, vol. 2, no. 6, pp. 1–13, Nov. 2020, https://doi.org/10.1148/ryai.2020200010.
[30] S. Makrogiannis, K. W. Fishbein, A. Z. Moore, R. G. Spencer, and L. Ferrucci, “Image-based tissue distribution modeling for skeletal muscle quality characterization,” IEEE Trans Biomed Eng, vol. 63, no. 4, pp. 805–813, Apr. 2016, https://doi.org/10.1109/TBME.2015.2474305.
[31] F. Greco and C. A. Mallio, “Artificial intelligence and abdominal adipose tissue analysis: A literature review,” Quantitative Imaging in Medicine and Surgery, vol. 11, no. 10, p. 4461, 2021, https://doi.org/10.21037/qims-21-370.
[32] T. Kawai, M. V. Autieri, and R. Scalia, “Adipose tissue inflammation and metabolic dysfunction in obesity,” Am J Physiol Cell Physiol, vol. 320, no. 3, pp. C375–C391, Mar. 2021, https://doi.org/10.1152/ajpcell.00379.2020.
[33] Q. Zhang, J. Zhou, B. Zhang, W. Jia, and E. Wu, “Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets with a Morphological Processing Layer,” IEEE Access, vol. 8, pp. 128032–128041, 2020, https://doi.org/10.1109/ACCESS.2020.3008190.
[34] V. Del Vecchio et al., “Mitochondrial transfer from Adipose stem cells to breast cancer cells drives multi-drug resistance,” Journal of Experimental and Clinical Cancer Research, vol. 43, no. 1, Dec. 2024, https://doi.org/10.1186/s13046-024-03087-8.
[35] I. Galić, M. Habijan, H. Leventić, and K. Romić, “Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods,” Electronics, vol. 12, no. 21, p. 4411, 2023, https://doi.org/10.3390/electronics12214411.
[36] Z. Huang et al., “Cardiac Adipose Tissue Segmentation via Image-Level Annotations,” IEEE J Biomed Health Inform, vol. 27, no. 6, pp. 2932–2943, Jun. 2023, https://doi.org/10.1109/JBHI.2023.3263838.
[37] M. Wu, D. Junker, R. T. Branca, and D. C. Karampinos, “Magnetic Resonance Imaging Techniques for Brown Adipose Tissue Detection,” Frontiers in endocrinology, vol. 11, no. 421, 2020, https://doi.org/10.3389/fendo.2020.00421.
[38] F. Commandeur et al., “Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue from Non-Contrast CT,” IEEE Trans Med Imaging, vol. 37, no. 8, pp. 1835–1846, Aug. 2018, https://doi.org/10.1109/TMI.2018.2804799.
[39] J. W. Willows et al., “Visualization and analysis of whole depot adipose tissue neural innervation,” iScience, vol. 24, no. 10, Oct. 2021, https://doi.org/10.1016/j.isci.2021.103127.
[40] J. G. Suárez-García, B. de C. Alonso, J. M. Hernández-López, S. S. Hidalgo-Tobón, P. Dies-Suárez, and P. W. So, “Automated MRI quantification of pediatric abdominal adipose tissue using convolutional neural networks and novel total intensity maps,” Biomed Signal Process Control, vol. 102, Apr. 2025, https://doi.org/10.1016/j.bspc.2024.107250.
[41] M. K. S. Leow et al., “Activated brown adipose tissue releases exosomes containing mitochondrial methylene tetrahydrofolate dehydrogenase (NADP dependent) 1-like protein (MTHFD1L),” Biosci Rep, vol. 42, no. 5, May 2022, https://doi.org/10.1042/BSR20212543.
[42] L. Y. Hsu, Z. Ali, H. Bagheri, F. Huda, B. A. Redd, and E. C. Jones, “Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework,” Tomography, vol. 9, no. 3, pp. 1041–1051, Jun. 2023, https://doi.org/10.3390/tomography9030085.
[43] G. Hamilton, D. L. Smith, M. Bydder, K. S. Nayak, and H. H. Hu, “MR properties of brown and white adipose tissues,” Journal of Magnetic Resonance Imaging, vol. 34, no. 2, pp. 468–473, Aug. 2011, https://doi.org/10.1002/jmri.22623.
[44] S. Yoon et al., “Recent advances in optical imaging through deep tissue: imaging probes and techniques,” Biomaterials Research, vol. 26, no. 1, p. 57, 2022, https://doi.org/10.1186/s40824-022-00303-4.
[45] F. A. Huber, F. Del Grande, S. Rizzo, G. Guglielmi, and R. Guggenberger, “MRI in the assessment of adipose tissues and muscle composition: How to use it,” Quantitative imaging in medicine and surgery, vol. 108, p. 1636, 2020, https://doi.org/10.21037/QIMS.2020.02.06.
[46] A. B. Shinde and E. Zaganjor, “AdipoAtlas: Mapping out human white adipose tissue,” Cell Rep Med, vol. 2, no. 10, Oct. 2021, https://doi.org/10.1016/j.xcrm.2021.100429.
[47] Y. X. Xu et al., “Whole-body adipose tissue multi-omic analyses in sheep reveal molecular mechanisms underlying local adaptation to extreme environments,” Commun Biol, vol. 6, no. 1, Dec. 2023, https://doi.org/10.1038/s42003-023-04523-9.
[48] L. R. Holmes et al., “In-vivo detection of white adipose tissue browning: a multimodality imaging approach,” Sci Rep, vol. 13, no. 1, Dec. 2023, https://doi.org/10.1038/s41598-023-42537-9.
[49] F. Boschi, A. Negri, A. Conti, P. Bernardi, S. Chirumbolo, and A. Sbarbati, “The human dermal white adipose tissue (dWAT) morphology: A multimodal imaging approach,” Annals of Anatomy, vol. 255, Aug. 2024, https://doi.org/10.1016/j.aanat.2024.152289.
[50] L. Sun et al., “Brown Adipose Tissue: Multimodality Evaluation by PET, MRI, Infrared Thermography, and Whole-Body Calorimetry (TACTICAL-II),” Obesity, vol. 27, no. 9, pp. 1434–1442, 2019, https://doi.org/10.1002/oby.22560.
[51] H. Drakesmith, E. Nemeth, and T. Ganz, “Ironing out Ferroportin,” Cell metabolism, vol. 22, no. 5, pp. 777-787, 2015, https://doi.org/10.1016/j.cmet.2015.09.006.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Heru Syah Putra, Husneni Mukhtar, Fenty Alia, Mas Rizky Anggun Adipurna Syamsunarno
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with JITEKI agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution 4.0 International License