Ground truth generation toolset for microscopic human embryo images

用于显微人类胚胎图像的地面实况生成工具集

• 批准号: 522819-2017
• 负责人: Ho, Paul
• 金额: $ 1.8万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640369
• 关键词: Ground; truth; generation; toolset; microscopic

The use of fertility treatments has increased dramatically in the last 4 decades as a result of delayedchildbearing. Unfortunately, female reproductive capacity declines from a peak in the 2nd and 3rd decades oflife so that by the age of 40 there is a significant reduction in fertility with a high chance of miscarriage.Assisted Reproductive Technology includes fertility treatments that involve handling eggs and embryos outsidethe body. In Vitro Fertilization (IVF) is one of the most common fertility treatments in which ovaries are hyperstimulated to produce multiple eggs for external fertilization. The fertilized eggs (embryos) are incubated untilthey reach the blastocyst stage (about 5 days after fertilization) and then are chosen for implantation. Optimalcandidates are evaluated during the incubation process by assigning grades to reflect the quality of blastocystsbased on intrinsic morphological structures. Being able to quantify why certain blastocysts have higherimplantation potentials will help researchers to increase success rates and minimize the chances of multiplebirths due to transferring multiple embryos. Therefore, it is essential to identify and measure various embryocomponents and developmental parameters in a reliable and accurate manner. The main focus of this researchproject is to create semi-automated technologies that can assist and facilitate expert embryologists to identifyand mark regions of interest in a high pace. More specifically, we propose developing an image processingtoolset that can automatically process some of the more tedious parts of ground truth identification and allowexpert to improve results with the minimal manual intervention/effort in 2D microscopic images of humanembryos.

在过去的40年里，由于晚育的原因，生育治疗的使用急剧增加。不幸的是，女性的生育能力在20岁和30岁时达到峰值，到40岁时，生育能力显著下降，流产的几率很高。辅助生殖技术包括在体外处理卵子和胚胎的生育治疗。体外受精（IVF）是最常见的生育治疗方法之一，通过过度刺激卵巢产生多个卵子进行体外受精。受精卵（胚胎）被孵育至囊胚期（受精后约5天），然后被选中进行着床。在培养过程中，根据囊胚的内在形态结构对最佳候选囊胚进行评分，以反映囊胚的质量。能够量化为什么某些囊胚具有更高的植入潜力将有助于研究人员提高成功率，并最大限度地减少由于移植多个胚胎而导致多胎分娩的机会。因此，可靠、准确地识别和测量各种胚胎成分和发育参数至关重要。该研究项目的主要重点是创造半自动化技术，帮助和促进胚胎学家专家快速识别和标记感兴趣的区域。更具体地说，我们建议开发一种图像处理工具集，该工具集可以自动处理地面真实识别中一些更繁琐的部分，并允许专家以最小的人工干预/努力改善人类胚胎二维显微图像的结果。