Preimplantation Blastomere Boundary Identification in HMC Microscopic Images of Early Stage Human Embryos

TL;DR

This paper introduces a new model-based iterative method for accurately identifying blastomere boundaries in early-stage human embryo images, addressing challenges like cell overlap and fragmentation.

Contribution

The authors developed a novel iterative approach modeling blastomeres as ellipses, improving boundary detection in complex microscopic embryo images.

Findings

Achieved 92% precision in boundary detection.

Attained 88% sensitivity across test images.

Overall quality score of 83% on diverse datasets.

Abstract

We present a novel method for identification of the boundary of embryonic cells (blastomeres) in Hoffman Modulation Contrast (HMC) microscopic images that are taken between day one to day three. Identification of boundaries of blastomeres is a challenging task, especially in the cases containing four or more cells. This is because these cells are bundled up tightly inside an embryo's membrane and any 2D image projection of such 3D embryo includes cell overlaps, occlusions, and projection ambiguities. Moreover, human embryos include fragmentation, which does not conform to any specific patterns or shape. Here we developed a model-based iterative approach, in which blastomeres are modeled as ellipses that conform to the local image features, such as edges and normals. In an iterative process, each image feature contributes only to one candidate and is removed upon being associated to a model candidate. We have tested the proposed algorithm on an image dataset comprising of 468 human embryos obtained from different sources. An overall Precision, Sensitivity and Overall Quality (OQ) of 92%, 88% and 83% are achieved.