From Cells to Batteries: Semantic Segmentation's Evolution in Quality Control

In an unexpected leap forward for the world of battery technology, an investigation that was first introduced at the Materials Research Society (MRS) conference in 2019 has evolved into a cutting-edge computational tool with potential application to the quality and reliability of batteries. This remarkable innovation leverages the power of semantic segmentation, a sophisticated image analysis technique, to detect and monitor crucial morphological structures such as dendrites during the cycling of batteries.<br/><br/>The journey of semantic segmentation in the realm of science and technology has been nothing short of remarkable. It all began with pioneering efforts to apply this technique to understanding and detecting abnormal cells in the early 2000s [1]. Researchers ventured into the intricate world of "hairy cells" in leukemia, demonstrating the potential of semantic segmentation to dissect and analyze complex biological structures with unprecedented precision.<br/><br/>For over a decade, scientists delved into the mysteries of brain cells, harnessing semantic segmentation to unravel the intricate shapes of brain cells and neuroproteins that power our minds. Their tireless efforts culminated in a groundbreaking paper published in the esteemed Neuroimage journal, earning them the prestigious Precision Medicine World Conference Pioneer Award in 2023 [2]. This recognition marked a turning point in the field, solidifying the importance of semantic segmentation in advancing our understanding of radioactive tags to be used in the human brain.<br/><br/>Now, this transformative technology has taken another leap into the world of energy storage. Researchers have successfully adapted semantic segmentation to scrutinize the inner workings of batteries, specifically targeting dendrites, which have long been a challenge in the realm of battery research. The results of this innovative work were published in the prestigious Nature Partner Journal Computational Materials journal in 2023 [3]*, showcasing the immense potential of semantic segmentation to revolutionize battery quality control.<br/>The implications of this breakthrough are immense. Imagine batteries that are not only more powerful but also safer, thanks to the ability to detect and mitigate dendrite-related issues before they become problematic. This advancement promises to usher in a new era of protocols for testing energy storage devices. <br/><br/>As the research community eagerly awaits further details on the practical applications of this technology, one thing is clear: the future of batteries is brighter than ever, thanks to the extraordinary journey of semantic segmentation from leukemia cells to brain cells and now to the heart of our energy solutions. Stay tuned for more updates on this development that promises to reshape how we understand morphologies within batteries.<br/><br/>*This work was funded by the Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. <br/><br/>[1] Ushizima Sabino D M, Costa LF, Zago MA (2003), Automatic leukemia diagnostic, Acta Microscopica (12)1:1-6<br/>[2] Ushizima, Chen, Alegro, Ovando, Eser, Lee, Poon, Shankar, Kantamneni, Satrawada, Amaro Jr, Heinsen, Tosun, Grinberg, "Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation", NeuroImage 2022<br/>[3] Huang, Perlmutter, Su, Quenum, Shevchenko, Parkinson, Zenyuk, Ushizima, “Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning”. Nature Computational Materials (2023)