已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization

胚泡 体外受精 透明带 分割 人工智能 囊胚腔 生殖技术 男科 内细胞团 计算机科学 生物 胚胎 模式识别(心理学) 胚胎发生 医学 遗传学 卵母细胞
作者
Muhammad Ishaq,Salman Raza,Hunza Rehar,Shan e Zain ul Abadeen,Dildar Hussain,Rizwan Ali Naqvi,Seung Won Lee
出处
期刊:Mathematics [Multidisciplinary Digital Publishing Institute]
卷期号:11 (9): 2023-2023
标识
DOI:10.3390/math11092023
摘要

The increasing global infertility rate is a matter of significant concern. In vitro fertilization (IVF) significantly minimizes infertility by providing an alternative clinical means of becoming pregnant. The success of IVF mainly depends on the assessment and analysis of human blastocyst components such as the blastocoel (BC), zona pellucida (ZP), inner cell mass (ICM), and trophectoderm (TE). Embryologists perform a morphological assessment of the blastocyst components for the selection of potential embryos to be used in the IVF process. Manual assessment of blastocyst components is time-consuming, subjective, and prone to errors. Therefore, artificial intelligence (AI)-based methods are highly desirable for enhancing the success rate and efficiency of IVF. In this study, a novel feature-supplementation-based blastocyst segmentation network (FSBS-Net) has been developed to deliver higher segmentation accuracy for blastocyst components with less computational overhead compared with state-of-the-art methods. FSBS-Net uses an effective feature supplementation mechanism along with ascending channel convolutional blocks to accurately detect the pixels of the blastocyst components with minimal spatial loss. The proposed method was evaluated using an open database for human blastocyst component segmentation, and it outperformed state-of-the-art methods in terms of both segmentation accuracy and computational efficiency. FSBS-Net segmented the BC, ZP, ICM, TE, and background with intersections over union (IoU) values of 89.15, 85.80, 85.55, 80.17, and 95.61%, respectively. In addition, FSBS-Net achieved a mean IoU for all categories of 87.26% with only 2.01 million trainable parameters. The experimental results demonstrate that the proposed method could be very helpful in assisting embryologists in the morphological assessment of human blastocyst components.
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
chanyi完成签到,获得积分10
1秒前
hqy完成签到,获得积分10
1秒前
张豪杰完成签到 ,获得积分10
2秒前
lht完成签到 ,获得积分10
4秒前
在水一方应助hyr采纳,获得10
4秒前
5秒前
潇洒的语蝶完成签到 ,获得积分10
6秒前
ccc完成签到,获得积分10
7秒前
多多完成签到 ,获得积分10
7秒前
8秒前
TMOMOR应助桃李春风一杯酒采纳,获得10
9秒前
9秒前
9秒前
JHL完成签到 ,获得积分10
10秒前
温柔晓刚完成签到,获得积分10
10秒前
闪闪璎关注了科研通微信公众号
11秒前
哈哈完成签到,获得积分20
11秒前
11秒前
英姑应助受伤翠容采纳,获得30
12秒前
13秒前
踏实嚣完成签到 ,获得积分10
13秒前
14秒前
hqy发布了新的文献求助50
14秒前
沉默白猫完成签到 ,获得积分10
15秒前
恰逢时年完成签到,获得积分10
15秒前
伶俐的金连完成签到 ,获得积分10
16秒前
慢歌完成签到 ,获得积分10
16秒前
余子健完成签到 ,获得积分10
17秒前
Lucas应助哈哈采纳,获得10
17秒前
fsznc1完成签到 ,获得积分0
18秒前
hyr发布了新的文献求助10
18秒前
恰逢时年发布了新的文献求助10
18秒前
丘比特应助仲十三采纳,获得10
20秒前
受伤翠容完成签到,获得积分10
20秒前
自觉醉香完成签到 ,获得积分10
22秒前
22秒前
吞吞完成签到 ,获得积分10
23秒前
幸福的雪枫完成签到 ,获得积分10
24秒前
橘橘橘子皮完成签到 ,获得积分10
24秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
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 3976560
求助须知:如何正确求助?哪些是违规求助? 3520659
关于积分的说明 11204287
捐赠科研通 3257271
什么是DOI,文献DOI怎么找? 1798653
邀请新用户注册赠送积分活动 877835
科研通“疑难数据库(出版商)”最低求助积分说明 806570