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001-es BibID:	BIBFORM074341
035-os BibID:	(WOS)000454454600014 (Scopus)85049921240
Első szerző:	Török Péter (szülész-nőgyógyász)
Cím:	Digital Image Analysis with Fully Connected Convolutional Neural Network to Facilitate Hysteroscopic Fibroid Resection / Péter Török, Balázs Harangi
Dátum:	2018
ISSN:	0378-7346
Megjegyzések:	AimsTo determine the accuracy of deep neural network in identifying the plane between myoma and normal myometrium.MethodsOn the images of surgery, different structures were signed and annotated for the training phase. After the appropriate training of the deep neural network with 4688 images from that training set, 1600 formerly unseen images were used for testing.Indication for surgery was heavy menstrual bleeding and hysteroscopic finding was submucous fibroid. Operative intervention was fibroid resection. Recorded videos of transcervical resection of myoma in 13 cases were used for the study. Different filters and procedures were applied by the fully convolutional neural network (FCNN) for identifying previously annotated structures.ResultsPreviously manually annotated images and the manually drawn bitmasks were used for training the applied fully convolutional neural network and then this pre-trained network was used for automatic segmentation of normal myometrium in an unseen video frame. The segmentation pixel-wise accuracy achieved the 86.19% considering the Hausdorff metric.ConclusionUsing deep learning technique in analyzing process of endoscopic video frame could help in real-time identification of structures while performing endoscopic surgery.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk convolutional neural network deep learning endoscopy hysteroscopy fibroid
Megjelenés:	Gynecologic And Obstetric Investigation. - 83 : 6 (2018), p. 615-619. -
További szerzők:	Harangi Balázs (1986-) (programtervező matematikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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