Weakly supervised multi-class semantic video segmentation for road scenes

Awan, Mehwish; Shin, Jitae

doi:10.1016/j.cviu.2023.103664

Cited by 1 publication

(1 citation statement)

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“…While alternative tracking algorithms, such as the nearest-neighbor approach, may suffice for the purposes of establishing temporal connectivity, they may not explicitly track lineage without adaptation. Instead, we implement a topological methodology to avoid this drawback and any additional parameterization these tracking approaches may bring (46) . Starting with the initial frame, we iterate over each pixel in each object and compare it with the same pixels in the following frame.…”

Section: Spatiotemporal Segmentation By Temporal Tobleronementioning

confidence: 99%

Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy

Panconi,

Tansell,

Collins

et al. 2023

Biol. Imaging

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Image analysis techniques provide objective and reproducible statistics for interpreting microscopy data. At higher dimensions, three-dimensional (3D) volumetric and spatiotemporal data highlight additional properties and behaviors beyond the static 2D focal plane. However, increased dimensionality carries increased complexity, and existing techniques for general segmentation of 3D data are either primitive, or highly specialized to specific biological structures. Borrowing from the principles of 2D topological data analysis (TDA), we formulate a 3D segmentation algorithm that implements persistent homology to identify variations in image intensity. From this, we derive two separate variants applicable to spatial and spatiotemporal data, respectively. We demonstrate that this analysis yields both sensitive and specific results on simulated data and can distinguish prominent biological structures in fluorescence microscopy images, regardless of their shape. Furthermore, we highlight the efficacy of temporal TDA in tracking cell lineage and the frequency of cell and organelle replication.

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Section: Spatiotemporal Segmentation By Temporal Tobleronementioning

confidence: 99%