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.

该研究通过实例分割神经系统的体积光学显微图像，为神经科学的突破性研究提供便利，能够在细胞分辨率上促进神经回路的功能和形态分析。为了解决多神经元光学显微数据的现有挑战，研究团队发布了第一个公开可用的具有像素级注释的多神经元光学显微数据集，并定义了一套实例分割评估指标，以推动机器学习方法在捕捉长程数据依赖性方面的方法研究，并促进基础神经科学的科学发现。