Physics-based machine learning for subcellular segmentation in living cells
AuthorSekh, Arif Ahmed; Opstad, Ida Sundvor; Godtliebsen, Gustav; Birgisdottir, Åsa Birna; Ahluwalia, Balpreet Singh; Agarwal, Krishna; Prasad, Dilip K.
Segmenting subcellular structures in living cells from fluorescence microscope images is a ground truth (GT)-deficient problem. The microscopes’ three-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution and the complex morphological manifestations of the structures all contribute to GT-hardness. Unsupervised segmentation approaches are quite inaccurate. Therefore, manual segmentation relying on heuristics and experience remains the preferred approach. However, this process is tedious, given the countless structures present inside a single cell, and generating analytics across a large population of cells or performing advanced artificial intelligence tasks such as tracking are greatly limited. Here we bring modelling and deep learning to a nexus for solving this GT-hard problem, improving both the accuracy and speed of subcellular segmentation. We introduce a simulation-supervision approach empowered by physics-based GT, which presents two advantages. First, the physics-based GT resolves the GT-hardness. Second, computational modelling of all the relevant physical aspects assists the deep learning models in learning to compensate, to a great extent, for the limitations of physics and the instrument. We show extensive results on the segmentation of small vesicles and mitochondria in diverse and independent living- and fixed-cell datasets. We demonstrate the adaptability of the approach across diverse microscopes through transfer learning, and illustrate biologically relevant applications of automated analytics and motion analysis.
CitationSekh AA, Opstad IS, Godtliebsen G, Birgisdottir Åb, Ahluwalia BS, Agarwal K, Prasad DK. Physics-based machine learning for subcellular segmentation in living cells. Nature Machine Intelligence. 2021:1-12
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