Cell identification in whole-brain multiview images of neural activation
This work addresses the challenge of accurate cell identification in neuroscience imaging, which is crucial for mapping neural activation, but it appears incremental as it builds on existing techniques like mean shift and semantic deconvolution.
The authors tackled the problem of identifying brain cells in whole-brain multiview microscopy images by developing a scalable algorithmic pipeline that includes hierarchical registration and a novel multiview semantic deconvolution method, achieving an F1 measure of 0.89 on a test volume with 3278 annotated cells.
We present a scalable method for brain cell identification in multiview confocal light sheet microscopy images. Our algorithmic pipeline includes a hierarchical registration approach and a novel multiview version of semantic deconvolution that simultaneously enhance visibility of fluorescent cell bodies, equalize their contrast, and fuses adjacent views into a single 3D images on which cell identification is performed with mean shift. We present empirical results on a whole-brain image of an adult Arc-dVenus mouse acquired at 4micron resolution. Based on an annotated test volume containing 3278 cells, our algorithm achieves an $F_1$ measure of 0.89.