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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
啾啾啾发布了新的文献求助10
1秒前
CHSLN完成签到 ,获得积分10
2秒前
biofresh发布了新的文献求助30
2秒前
2秒前
3秒前
超级无敌奥特大王完成签到,获得积分10
3秒前
NexusExplorer应助小包子采纳,获得10
3秒前
努力向前看完成签到,获得积分10
5秒前
5秒前
5秒前
agnes完成签到,获得积分10
6秒前
失眠的向日葵完成签到 ,获得积分10
6秒前
大橙子发布了新的文献求助10
7秒前
9秒前
10秒前
qq完成签到,获得积分10
11秒前
王二哈完成签到,获得积分10
12秒前
行者无疆发布了新的文献求助10
13秒前
令散内方完成签到,获得积分10
13秒前
外向的雁玉完成签到,获得积分10
13秒前
慧灰huihui发布了新的文献求助10
14秒前
Ava应助Desire采纳,获得10
15秒前
量子星尘发布了新的文献求助10
18秒前
风信子完成签到,获得积分10
18秒前
小熊完成签到 ,获得积分10
20秒前
24秒前
shu完成签到,获得积分10
24秒前
24秒前
勤奋的毛豆完成签到,获得积分10
27秒前
行者无疆完成签到,获得积分10
27秒前
28秒前
Jackylee完成签到,获得积分10
28秒前
careyzhou发布了新的文献求助10
29秒前
舒心之云完成签到,获得积分10
31秒前
Desire发布了新的文献求助10
31秒前
独自受罪完成签到 ,获得积分10
32秒前
甘蓝型油菜完成签到,获得积分10
33秒前
Distance发布了新的文献求助10
34秒前
大橙子发布了新的文献求助10
35秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038201
求助须知:如何正确求助?哪些是违规求助? 3575940
关于积分的说明 11373987
捐赠科研通 3305747
什么是DOI,文献DOI怎么找? 1819274
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022