{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T15:44:59Z","timestamp":1779291899594,"version":"3.51.4"},"reference-count":90,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T00:00:00Z","timestamp":1753488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"US Department of Energy (DOE) Office of Science Advanced Scientific Computing Research (ASCR)","award":["DE-AC02-05CH11231"],"award-info":[{"award-number":["DE-AC02-05CH11231"]}]},{"name":"Office of Basic Energy Sciences (BES)","award":["DE-AC02-05CH11231"],"award-info":[{"award-number":["DE-AC02-05CH11231"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model\u2019s foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.<\/jats:p>","DOI":"10.3390\/jimaging11080252","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T08:51:33Z","timestamp":1753692693000},"page":"252","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3528-5874","authenticated-orcid":false,"given":"Zineb","family":"Sordo","sequence":"first","affiliation":[{"name":"Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7443-5721","authenticated-orcid":false,"given":"Eric","family":"Chagnon","sequence":"additional","affiliation":[{"name":"Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5365-7351","authenticated-orcid":false,"given":"Zixi","family":"Hu","sequence":"additional","affiliation":[{"name":"Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7173-0174","authenticated-orcid":false,"given":"Jeffrey J.","family":"Donatelli","sequence":"additional","affiliation":[{"name":"Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3554-2008","authenticated-orcid":false,"given":"Peter","family":"Andeer","sequence":"additional","affiliation":[{"name":"Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4180-9397","authenticated-orcid":false,"given":"Peter S.","family":"Nico","sequence":"additional","affiliation":[{"name":"Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8404-3259","authenticated-orcid":false,"given":"Trent","family":"Northen","sequence":"additional","affiliation":[{"name":"Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7363-9468","authenticated-orcid":false,"given":"Daniela","family":"Ushizima","sequence":"additional","affiliation":[{"name":"Applied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"},{"name":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA"},{"name":"Berkeley Institute for Data Science, University of California, Berkeley, CA 94720, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"ref_1","unstructured":"Sordo, Z., Chagnon, E., and Ushizima, D. (2025). A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images. arXiv."},{"key":"ref_2","unstructured":"Foster, D. (2023). Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play, O\u2019Reilly Media. [2nd ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/s41586-024-07930-y","article-title":"Larger and more instructable language models become less reliable","volume":"634","author":"Zhou","year":"2024","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1057\/s41599-024-03811-x","article-title":"AI hallucination: Towards a comprehensive classification of distorted information in artificial intelligence-generated content","volume":"11","author":"Sun","year":"2024","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MIS.2024.3439109","article-title":"The Longtail Impact of Generative AI on Disinformation: Harmonizing Dichotomous Perspectives","volume":"39","author":"Lucas","year":"2024","journal-title":"IEEE Intell. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maleki, N., Padmanabhan, B., and Dutta, K. (2024, January 25\u201327). AI Hallucinations: A Misnomer Worth Clarifying. Proceedings of the 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore.","DOI":"10.1109\/CAI59869.2024.00033"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.eswa.2018.05.015","article-title":"Reverse image search for scientific data within and beyond the visible spectrum","volume":"109","author":"Araujo","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hook, D.W., Porter, S.J., and Herzog, C. (2018). Dimensions: Building Context for Search and Evaluation. Front. Res. Metr. Anal., 3.","DOI":"10.3389\/frma.2018.00023"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_13","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017, January 4\u20139). Attention is All you Need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_14","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019). Self-Attention Generative Adversarial Networks. arXiv."},{"key":"ref_15","unstructured":"Bach, F., and Blei, D. (2015, January 7\u20139). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning, Lille, France. Proceedings of Machine Learning Research."},{"key":"ref_16","first-page":"6840","article-title":"Denoising Diffusion Probabilistic Models","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_17","unstructured":"Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I. (2021). Zero-Shot Text-to-Image Generation. arXiv."},{"key":"ref_18","unstructured":"OpenAI (2024, February 27). CLIP: Connecting Text and Images. Available online: https:\/\/openai.com\/index\/clip\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2021). High-Resolution Image Synthesis with Latent Diffusion Models. arXiv.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_20","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021). Learning Transferable Visual Models From Natural Language Supervision. arXiv."},{"key":"ref_21","unstructured":"Betker, J., Goh, G., Jing, L., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., and Guo, Y. (2023, January 8\u201310). Improving Image Generation with Better Captions. Proceedings of the OpenAI Library, Montreal, QC, Canada."},{"key":"ref_22","unstructured":"Spataro, J. (2025, July 15). Introducing Microsoft 365 Copilot\u2014Your Copilot for Work. Available online: https:\/\/blogs.microsoft.com\/blog\/2023\/03\/16\/introducing-microsoft-365-copilot-your-copilot-for-work\/."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023). Segment Anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_24","unstructured":"Mukherjee, S., Lang, J., Kwon, O., Zenyuk, I., Brogden, V., Weber, A., and Ushizima, D. (2025). Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data. arXiv."},{"key":"ref_25","unstructured":"Kokhlikyan, N., Jayaraman, B., Bordes, F., Guo, C., and Chaudhuri, K. (2023). Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Girdhar, R., Singh, M., Brown, A., Duval, Q., Azadi, S., Rambhatla, S.S., Shah, A., Yin, X., Parikh, D., and Misra, I. (2024). Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning. arXiv.","DOI":"10.1007\/978-3-031-73033-7_12"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sheynin, S., Polyak, A., Singer, U., Kirstain, Y., Zohar, A., Ashual, O., Parikh, D., and Taigman, Y. (2024, January 16\u201322). Emu Edit: Precise Image Editing via Recognition and Generation Tasks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00847"},{"key":"ref_28","unstructured":"Wang, L., Li, S., Yang, F., Wang, J., Zhang, Z., Liu, Y., Wang, Y., and Yang, J. (2025). Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability. arXiv."},{"key":"ref_29","unstructured":"Zhang, L., You, W., Shi, K., and Gu, S. (2025). Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110763","DOI":"10.1016\/j.patcog.2024.110763","article-title":"Unsupervised multi-branch network with high-frequency enhancement for image dehazing","volume":"156","author":"Sun","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_31","unstructured":"Kingma, D.P., and Welling, M. (2022). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Emm, T.A., and Zhang, Y. (2024). Self-Adaptive Evolutionary Info Variational Autoencoder. Computers, 13.","DOI":"10.3390\/computers13080214"},{"key":"ref_33","unstructured":"Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A. (2017, January 24\u201326). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_34","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_35","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_36","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_37","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_38","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, 5\u20139 October 2015"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kang, M., Zhu, J.Y., Zhang, R., Park, J., Shechtman, E., Paris, S., and Park, T. (2023). Scaling up GANs for Text-to-Image Synthesis. arXiv.","DOI":"10.1109\/CVPR52729.2023.00976"},{"key":"ref_40","unstructured":"Wang, P., Bardy, N., and Hamilton, K.G. (2025, July 15). lucidrains\/Gigagan-Pytorch. Available online: https:\/\/github.com\/lucidrains\/gigagan-pytorch."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D. (2017, January 22\u201329). StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., and He, X. (2018, January 18\u201323). AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00143"},{"key":"ref_43","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (May, January 30). Progressive Growing of GANs for Improved Quality, Stability, and Variation. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_44","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2019, January 6\u20139). Large Scale GAN Training for High Fidelity Natural Image Synthesis. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kulkarni, A., Shivananda, A., Kulkarni, A., and Gudivada, D. (2023). Diffusion Model and Generative AI for Images. Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs, Apress.","DOI":"10.1007\/978-1-4842-9994-4"},{"key":"ref_46","unstructured":"Song, Y., and Ermon, S. (2019, January 8\u201314). Generative Modeling by Estimating Gradients of the Data Distribution. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_47","unstructured":"Song, Y., and Ermon, S. (2021, January 4). Score-Based Generative Modeling Through Stochastic Differential Equations. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_48","first-page":"695","article-title":"Estimation of Non-Normalized Statistical Models by Score Matching","volume":"6","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_49","unstructured":"Karras, T., Aittala, M., Aila, T., and Laine, S. (2022, January 9). Elucidating the Design Space of Diffusion-Based Generative Models. Proceedings of the Advances in Neural Information Processing Systems, New Orleans, LA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/0304-4149(82)90051-5","article-title":"Reverse-time diffusion equation models","volume":"12","author":"Anderson","year":"1982","journal-title":"Stoch. Process. Their Appl."},{"key":"ref_51","unstructured":"Chambon, B., Raghunathan, A., and Ermon, S. (2023, January 1\u20135). Flow Matching for Generative Modeling. Proceedings of the International Conference on Learning Representations (ICLR), Kigali, Rwanda."},{"key":"ref_52","unstructured":"Tian, K., Jiang, Y., Yuan, Z., Peng, B., and Wang, L. (2024, January 16). Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_53","first-page":"8780","article-title":"Diffusion Models Beat GANs on Image Synthesis","volume":"Volume 34","author":"Ranzato","year":"2021","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_54","unstructured":"Nichol, A.Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., and Chen, M. (2022, January 17\u201323). GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. Proceedings of the 39th International Conference on Machine Learning Research, Baltimore, MD, USA."},{"key":"ref_55","first-page":"36479","article-title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","volume":"Volume 35","author":"Koyejo","year":"2022","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_56","unstructured":"Ho, J., and Salimans, T. (2025, July 15). Classifier-Free Diffusion Guidance. In Proceedings of the NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021. Available online: https:\/\/openreview.net\/pdf?id=qw8AKxfYbI."},{"key":"ref_57","unstructured":"Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., and Cohen-or, D. (2023, January 1\u20135). Prompt-to-Prompt Image Editing with Cross-Attention Control. Proceedings of the The Eleventh International Conference on Learning Representations, Kigali, Rwanda."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Brooks, T., Holynski, A., and Efros, A.A. (2022). InstructPix2Pix: Learning to Follow Image Editing Instructions. arXiv.","DOI":"10.1109\/CVPR52729.2023.01764"},{"key":"ref_59","unstructured":"Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I. (2021, January 18\u201324). Zero-Shot Text-to-Image Generation. Proceedings of the 38th International Conference on Machine Learning Research, Virtual."},{"key":"ref_60","unstructured":"Rolfe, J.T. (2017, January 24\u201326). Discrete Variational Autoencoders. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_61","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv."},{"key":"ref_62","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning Research, Virtual."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., and Agrawala, M. (2023, January 1\u20136). Adding Conditional Control to Text-to-Image Diffusion Models. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"ref_64","unstructured":"AI, S. (2025, July 15). Stable Diffusion 2-1-unCLIP Model Card. Available online: https:\/\/huggingface.co\/stabilityai\/stable-diffusion-2-1-unclip."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 18\u201324). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_66","unstructured":"Couairon, G., Verbeek, J., Schwenk, H., and Cord, M. (2023, January 1\u20135). DiffEdit: Diffusion-based semantic image editing with mask guidance. Proceedings of the The Eleventh International Conference on Learning Representations, Kigali, Rwanda."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Brack, M., Friedrich, F., Kornmeier, K., Tsaban, L., Schramowski, P., Kersting, K., and Passos, A. (2024, January 16\u201322). LEDITS++: Limitless Image Editing using Text-to-Image Models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00846"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Peebles, W., and Xie, S. (2023, January 2\u20133). Scalable Diffusion Models with Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"ref_69","unstructured":"Ho, J., and Salimans, T. (2022). Classifier-Free Diffusion Guidance. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 13\u201319). Analyzing and Improving the Image Quality of StyleGAN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_71","unstructured":"Naeem, M.F., Oh, S.J., Uh, Y., Choi, Y., and Yoo, J. (2020, January 13\u201318). Reliable Fidelity and Diversity Metrics for Generative Models. Proceedings of the 37th International Conference on Machine Learning Research, Virtual."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2950","DOI":"10.1109\/TIP.2024.3385295","article-title":"High-Quality and Diverse Few-Shot Image Generation via Masked Discrimination","volume":"33","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_73","first-page":"118876","article-title":"Deep learning for Alzheimer\u2019s disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation","volume":"274","author":"Ushizima","year":"2022","journal-title":"NeuroImage"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4084","DOI":"10.1109\/JBHI.2024.3385504","article-title":"Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis","volume":"28","author":"Dorjsembe","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., and Yuan, Y. (2022). Brain Imaging Generation with Latent Diffusion Models. Proceedings of the Deep Generative Models, Singapore, 22 September 2022, Springer.","DOI":"10.1007\/978-3-031-18576-2"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Moghadam, P.A., Dalen, S.V., Martin, K.C., Lennerz, J., Yip, S., Farahani, H., and Bashashati, A. (2022). A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images. arXiv.","DOI":"10.1109\/WACV56688.2023.00204"},{"key":"ref_77","unstructured":"Song, J., Meng, C., and Ermon, S. (2020). Denoising Diffusion Implicit Models. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ushizima, D.M., Ajo-Franklin, J., Macdowell, A., Morozov, D., Nico, P., Parkinson, B., Bethel, E.W., and Sethian, J.A. (2011, January 23\u201325). Statistical segmentation and porosity quantification of 3D x-ray microtomography. Proceedings of the SPIE Optics and Photonics: XXXIV Applications of Digital Image Processing, San Diego, CA, USA.","DOI":"10.1117\/12.892809"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/s41597-022-01119-6","article-title":"A reusable neural network pipeline for unidirectional fiber segmentation","volume":"9","author":"Ushizima","year":"2022","journal-title":"Nat. Sci. Data"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Sordo, Z., Andeer, P., Sethian, J., Northen, T., and Ushizima, D. (2024). RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-63497-8"},{"key":"ref_81","unstructured":"Ushizima, D., Weber, G., Ajo-Franklin, J., Kim, Y., Macdowell, A., Morozov, D., Nico, P., Parkinson, D., Trebotich, D., and Wan, J. (2011, January 10\u201314). Analysis and visualization for multiscale control of geologic CO2. Proceedings of the Scientific Discovery through Advanced Computing (SciDAC\u20192011), Denver, CO, USA."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"102872","DOI":"10.1016\/j.media.2023.102872","article-title":"Adaptive diffusion priors for accelerated MRI reconstruction","volume":"88","author":"Dar","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_83","first-page":"188","article-title":"Diffusion-Based Domain Adaptation for Medical Image Segmentation Using Stochastic Step Alignment","volume":"Volume 15008","author":"Linguraru","year":"2024","journal-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention-MICCAI 2024\u201327th International Conference, Marrakesh, Morocco, 6\u201310 October 2024; Proceedings, Part VIII"},{"key":"ref_84","unstructured":"Karlinsky, L., Arbelle, A., Daniels, A., Nassar, A., Alfassi, A., Wu, B., Schwartz, E., Joshi, D., Kondic, J., and Shabtay, N. (2024). Granite Vision: A lightweight, open-source multimodal model for enterprise Intelligence. arXiv."},{"key":"ref_85","unstructured":"Dai, W., Lee, N., Wang, B., Yang, Z., Liu, Z., Barker, J., Rintamaki, T., Shoeybi, M., Catanzaro, B., and Ping, W. (2024). NVLM: Open Frontier-Class Multimodal LLMs. arXiv."},{"key":"ref_86","unstructured":"Ramalho, G.L.B., Ferreira, D.S., Bianchi, A.G.C., Carneiro, C.M., Medeiros, F.N.S., and Ushizima, D.M. (2025, July 15). Cell reconstruction under Voronoi and enclosing ellipses from 3D microscopy. IEEE Int. Symp. Biomed. Imaging (ISBI), Available online: https:\/\/cs.adelaide.edu.au\/~carneiro\/isbi15_challenge\/abstracts\/ushizima_2015.pdf."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1038\/s41597-021-00933-8","article-title":"Cric searchable image database as a public platform for conventional pap smear cytology data","volume":"8","author":"Rezende","year":"2021","journal-title":"Nat. Sci. Data"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"107164","DOI":"10.1016\/j.cemconres.2023.107164","article-title":"In-situ microtomography image segmentation for characterizing strain-hardening cementitious composites under tension using machine learning","volume":"169","author":"Xu","year":"2023","journal-title":"Cem. Concr. Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jneumeth.2017.03.002","article-title":"Automating Cell Detection and Classification in Human Brain Fluorescent Microscopy Images Using Dictionary Learning and Sparse Coding","volume":"282","author":"Alegro","year":"2017","journal-title":"J. Neurosci. Methods"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Zhao, K., Di, S., Lian, X., Li, S., Tao, D., Bessac, J., Chen, Z., and Cappello, F. (2020, January 10\u201313). SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378449"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/252\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:16:35Z","timestamp":1760033795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,26]]},"references-count":90,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["jimaging11080252"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11080252","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,26]]}}}