Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network
This work addresses embryo viability prediction for IVF clinics, but it is incremental as it applies a deep neural network to a known segmentation challenge in medical imaging.
The paper tackled the problem of segmenting the inner cell mass (ICM) and trophectoderm (TE) in human blastocyst images to aid embryo quality assessment in IVF, achieving high accuracy with metrics such as 99.1% accuracy for ICM and 98.3% for TE.
Embryo quality assessment based on morphological attributes is important for achieving higher pregnancy rates from in vitro fertilization (IVF). The accurate segmentation of the embryo's inner cell mass (ICM) and trophectoderm epithelium (TE) is important, as these parameters can help to predict the embryo viability and live birth potential. However, segmentation of the ICM and TE is difficult due to variations in their shape and similarities in their textures, both with each other and with their surroundings. To tackle this problem, a deep neural network (DNN) based segmentation approach was implemented. The DNN can identify the ICM region with 99.1% accuracy, 94.9% precision, 93.8% recall, a 94.3% Dice Coefficient, and a 89.3% Jaccard Index. It can extract the TE region with 98.3% accuracy, 91.8% precision, 93.2% recall, a 92.5% Dice Coefficient, and a 85.3% Jaccard Index.