@article {69518,
	title = {Hot-Distance: Combining One-Hot and Signed Distance Embeddings for Segmentation},
	journal = {arXiv},
	year = {2024},
	month = {2024 Jun 25},
	pages = {2406.17936},
	abstract = {<p>Machine learning models are only as good as the data to which they are fit. As such, it is always preferable to use as much data as possible in training models. What data can be used for fitting a model depends a lot on the formulation of the task. We introduce Hot-Distance, a novel segmentation target that incorporates the strength of signed boundary distance prediction with the flexibility of one-hot encoding, to increase the amount of usable training data for segmentation of subcellular structures in focused ion beam scanning electron microscopy (FIB-SEM).</p>
},
	doi = {10.48550/arXiv.2406.17936},
	url = {https://doi.org/10.48550/arXiv.2406.17936},
	author = {Marwan Zouinkhi and Jeff L. Rhoades and Aubrey V. Weigel}
}
@article {67774,
	title = {RS-FISH: Precise, interactive, fast, and scalable FISH spot detection},
	journal = {Nature Methods},
	volume = {19},
	year = {2022},
	month = {2022 Nov 17},
	pages = {1563-7},
	abstract = {<p>Fluorescent in-situ hybridization (FISH)-based methods are powerful tools to study molecular processes with subcellular resolution, relying on accurate identification and localization of diffraction-limited spots in microscopy images. We developed the Radial Symmetry-FISH (RS-FISH) software that accurately, robustly, and quickly detects single-molecule spots in two and three dimensions, making it applicable to several key assays, including single-molecule FISH (smFISH), spatial transcriptomics, and spatial genomics. RS-FISH allows interactive parameter tuning and scales to large sets of images as well as tera-byte sized image volumes such as entire brain scans using straight-forward distributed processing on workstations, clusters, and in the cloud.</p>
},
	doi = {10.1038/s41592-022-01669-y},
	author = {Ella Bahry and Laura Breimann and Marwan Zouinkhi and Leo Epstein and Klim Kolyvanov and Xi Long and Kyle I S Harrington and Timoth{\'e}e Lionnet and Stephan Preibisch}
}
