Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network
| Autor: | Aaron T. Ohta, Yousuf Harun, Thomas T. F. Huang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning In vitro fertilisation Jaccard index business.industry Computer Vision and Pattern Recognition (cs.CV) medicine.medical_treatment Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition Pattern recognition Embryo Electrical Engineering and Systems Science - Image and Video Processing Biology Machine Learning (cs.LG) medicine.anatomical_structure Sørensen–Dice coefficient FOS: Electrical engineering electronic engineering information engineering medicine Inner cell mass Segmentation Blastocyst Artificial intelligence business Embryo quality |
| Zdroj: | 2019 IEEE 13th International Conference on Nano/Molecular Medicine & Engineering (NANOMED). |
| DOI: | 10.1109/nanomed49242.2019.9130618 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|