Hatano Asuka, Someya Makoto, Tanaka Hiroaki, Sakakima Hiroki, Izumi Satoshi, Hoshijima Masahiko, Ellisman Mark, McCulloch Andrew D
Department of Mechanical Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656, Japan.
Department of Mechanical Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656, Japan.
J Struct Biol. 2022 Mar;214(1):107806. doi: 10.1016/j.jsb.2021.107806. Epub 2021 Nov 3.
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 µm. The volume distribution was very broad and skewed; the most frequent mitochondria were 0.04-0.06 µm, but mitochondria larger than 2.0 µm 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.
线粒体形态缺陷是患病心肌细胞的一个常见特征。然而，线粒体形态的定量评估受到电子显微镜（EM）图像耗时的手动分割的限制。为了进一步了解形态缺陷与功能障碍之间的关系，需要一种有效的形态重建方法，以便从EM图像中分离和重建线粒体。我们提出了一种从连续块面扫描电子显微镜（SBEM）图像中分离和重建单个线粒体的新方法。CDeep3M是一种基于云的用于EM图像的深度学习网络，用于分割线粒体内腔体积和边界。使用预测的内腔体积和外部边界进行后处理，以分离和重建单个线粒体。处理了来自两个单独心肌细胞的一系列SBEM图像。使用50个训练数据集时，最高F1分数为95%，高于先前报道的自动化方法，与手动分割相当。单个线粒体分离的像素级准确率为80%。对这两个系列SBEM图像中的总共2315个线粒体进行了评估，平均体积为0.78 µm。体积分布非常广泛且呈偏态；最常见的线粒体为0.04 - 0.06 µm，但大于2.0 µm的线粒体占总数的10%以上。平均短轴长度为0.47 µm。主要为纵向的线粒体（0 - 30度）占主导（54%）。这种新的自动分割和分离方法有助于定量线粒体形态，并增进对心肌细胞结构 - 功能关系的理解。
