  Instance segmentation of neurons in volumetric light microscopy images of
nervous systems enables groundbreaking research in neuroscience by facilitating
joint functional and morphological analyses of neural circuits at cellular
resolution. Yet said multi-neuron light microscopy data exhibits extremely
challenging properties for the task of instance segmentation: Individual
neurons have long-ranging, thin filamentous and widely branching morphologies,
multiple neurons are tightly inter-weaved, and partial volume effects, uneven
illumination and noise inherent to light microscopy severely impede local
disentangling as well as long-range tracing of individual neurons. These
properties reflect a current key challenge in machine learning research, namely
to effectively capture long-range dependencies in the data. While respective
methodological research is buzzing, to date methods are typically benchmarked
on synthetic datasets. To address this gap, we release the FlyLight Instance
Segmentation Benchmark (FISBe) dataset, the first publicly available
multi-neuron light microscopy dataset with pixel-wise annotations. In addition,
we define a set of instance segmentation metrics for benchmarking that we
designed to be meaningful with regard to downstream analyses. Lastly, we
provide three baselines to kick off a competition that we envision to both
advance the field of machine learning regarding methodology for capturing
long-range data dependencies, and facilitate scientific discovery in basic
neuroscience.
