Deep Learning Approach for Red Blood Cell Segmentation from Full-Field OCT Data of Human Skin

Mekonnen, B.K.B.K.MekonnenTsai, D.-F.D.-F.TsaiHsieh, T.-H.T.-H.HsiehYang, F.-L.F.-L.YangLiaw, S.-K.S.-K.LiawSHENG-LUNG HUANG2021-05-052021-05-052019https://www.scopus.com/inward/record.url?eid=2-s2.0-85075631747&partnerID=40&md5=45bbf5a4ab975f3f52742e3bd3c6174fhttps://scholars.lib.ntu.edu.tw/handle/123456789/559189The purpose of this paper is to segment red blood cells from the Full-Field OCT data of human skin, using deep learning technique. Test results show the developed technique is very promising for real time detection and counting of red blood cells. © 2019 IEEE.deep learning; Red blood cells; SegmentationBlood; Cells; Cytology; Image segmentation; Photonics; Human skin; Learning approach; Learning techniques; Real-time detection; Red blood cell; Deep learningDeep Learning Approach for Red Blood Cell Segmentation from Full-Field OCT Data of Human Skinconference paper10.1109/BioPhotonics.2019.88967482-s2.0-85075631747