Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Ryan Conrad, Kedar Narayan
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引用次数: 0

Abstract

Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train models on images that provide limited cellular contexts, precluding generality. To address this, we amassed a highly heterogeneous ∼1.5 × 106 image 2D unlabeled cellular EM dataset and segmented ∼135,000 mitochondrial instances therein. MitoNet, a model trained on these resources, performs well on challenging benchmarks and on previously unseen volume EM datasets containing tens of thousands of mitochondria. We release a Python package and napari plugin, empanada, to rapidly run inference, visualize, and proofread instance segmentations. A record of this paper's transparent peer review process is included in the supplemental information.

利用在不同数据集上训练的通用深度学习模型,对电子显微镜图像中的线粒体进行实例分割。
线粒体是一种多形性极强的细胞器。在任何二维或体积电子显微镜(EM)图像中准确无误地自动标注每个线粒体是一项尚未解决的计算挑战。目前基于深度学习的方法只能在提供有限细胞上下文的图像上训练模型,因而无法实现通用性。为了解决这个问题,我们收集了一个高度异构的 ∼1.5 × 106 像素的二维无标记细胞 EM 数据集,并在其中分割了 ∼135,000 个线粒体实例。在这些资源上训练出来的模型 MitoNet 在具有挑战性的基准测试和以前从未见过的包含数以万计线粒体的体积电磁数据集上表现出色。我们发布了一个 Python 软件包和 napari 插件 empanada,用于快速运行推理、可视化和校对实例分割。本文的同行评议过程记录载于补充信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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