Isolation and reconstruction of cardiac mitochondria from SBEM images using a deep learning-based method

分割 线粒体 人工智能 模式识别(心理学) 计算机科学 生物 计算机视觉 细胞生物学
作者
Asuka Hatano,Makoto Someya,Hiroaki Tanaka,Hiroki Sakakima,Satoshi Izumi,Masahiko Hoshijima,Mark H. Ellisman,Andrew D. McCulloch
出处
期刊:Journal of Structural Biology [Elsevier BV]
卷期号:214 (1): 107806-107806 被引量:3
标识
DOI:10.1016/j.jsb.2021.107806
摘要

Mitochondrial morphological defects are a common feature of diseased cardiac myocytes. However, quantitative assessment of mitochondrial morphology is limited by the time-consuming manual segmentation of electron micrograph (EM) images. To advance understanding of the relation between morphological defects and dysfunction, an efficient morphological reconstruction method is desired to enable isolation and reconstruction of mitochondria from EM images. We propose a new method for isolating and reconstructing single mitochondria from serial block-face scanning EM (SBEM) images. CDeep3M, a cloud-based deep learning network for EM images, was used to segment mitochondrial interior volumes and boundaries. Post-processing was performed using both the predicted interior volume and exterior boundary to isolate and reconstruct individual mitochondria. Series of SBEM images from two separate cardiac myocytes were processed. The highest F1-score was 95% using 50 training datasets, greater than that for previously reported automated methods and comparable to manual segmentations. Accuracy of separation of individual mitochondria was 80% on a pixel basis. A total of 2315 mitochondria in the two series of SBEM images were evaluated with a mean volume of 0.78 µm3. The volume distribution was very broad and skewed; the most frequent mitochondria were 0.04-0.06 µm3, but mitochondria larger than 2.0 µm3 accounted for more than 10% of the total number. The average short-axis length was 0.47 µm. Primarily longitudinal mitochondria (0-30 degrees) were dominant (54%). This new automated segmentation and separation method can help quantitate mitochondrial morphology and improve understanding of myocyte structure-function relationships.
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