Inner Cell Mass Segmentation in Human HMC Embryo Images using Fully Convolutional Network
Abstract
The success of In-Vitro Fertilization (IVF) greatly relies on the quality of the Inner Cell Mass (ICM) obtained at day 5 of embryo development. Unfortunately, ICM segmentation is difficult due to its shape variability and unconstrained profile. This paper proposes a two-stage pipeline that first uses a preprocessing step to remove artifacts from the input images which are then used by the Fully Convolutional Networks (FCN) to produce the ICM segmentation. The paper also proposes a novel data augmentation technique, specific to this application. The pre-processing step is shown to accelerate the learning of the FCN while data augmentation avoids overfitting and lead to better generalization. The performance of the proposed pipeline is evaluated on pixel classification accuracy and Jaccard index, on a dataset of 8460 images augmented from 235 images. The proposed method outperforms the state-of-art by about 28% on Jaccard index.
