التفاصيل البيبلوغرافية
| العنوان: |
Automatic identification and segmentation of slice of minimal hiatal dimensions in transperineal ultrasound volumes |
| المؤلفون: |
Claudia Manzini, Cornelis H. Slump, Anique T. M. Grob, C. H. van der Vaart, M. A. J. van Limbeek, F. van den Noort |
| المساهمون: |
Robotics and Mechatronics, TechMed Centre, Multi-Modality Medical Imaging |
| المصدر: |
Ultrasound in Obstetrics and Gynecology, 60(4), 570-576. Wiley |
| سنة النشر: |
2022 |
| مصطلحات موضوعية: |
Intraclass correlation, UT-Hybrid-D, Pelvic Organ Prolapse, Levator hiatus, automatic segmentation, Imaging, Three-Dimensional, Pregnancy, pelvic floor, medicine, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Transperineal ultrasound, Ultrasonography, Pelvic floor, Radiological and Ultrasound Technology, business.industry, levator hiatus, Obstetrics and Gynecology, deep learning, Pattern recognition, General Medicine, transperineal ultrasound, Obstetrics, Identification (information), medicine.anatomical_structure, Reproductive Medicine, Coronal plane, urogenital hiatus, Automatic segmentation, Female, Artificial intelligence, business, Algorithms |
| الوصف: |
To develop and validate a tool for automatic selection of the slice of minimal hiatal dimensions (SMHD) and segmentation of the urogenital hiatus (UH) in transperineal ultrasound (TPUS) volumes.Manual selection of the SMHD and segmentation of the UH was performed in TPUS volumes of 116 women with symptomatic pelvic organ prolapse (POP). These data were used to train two deep-learning algorithms. The first algorithm was trained to provide an estimation of the position of the SMHD. Based on this estimation, a slice was selected and fed into the second algorithm, which performed automatic segmentation of the UH. From this segmentation, measurements of the UH area (UHA), anteroposterior diameter (APD) and coronal diameter (CD) were computed automatically. The mean absolute distance between manually and automatically selected SMHD, the overlap (dice similarity index (DSI)) between manual and automatic UH segmentation and the intraclass correlation coefficient (ICC) between manual and automatic UH measurements were assessed on a test set of 30 TPUS volumes.The mean absolute distance between manually and automatically selected SMHD was 0.20 cm. All DSI values between manual and automatic UH segmentations were above 0.85. The ICC values between manual and automatic UH measurements were 0.94 (95% CI, 0.87-0.97) for UHA, 0.92 (95% CI, 0.78-0.97) for APD and 0.82 (95% CI, 0.66-0.91) for CD, demonstrating excellent agreement.Our deep-learning algorithms allowed reliable automatic selection of the SMHD and UH segmentation in TPUS volumes of women with symptomatic POP. These algorithms can be implemented in the software of TPUS machines, thus reducing clinical analysis time and simplifying the examination of TPUS data for research and clinical purposes. © 2021 The Authors. Ultrasound in Obstetricsamp; Gynecology published by John Wileyamp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| تدمد: |
0960-7692 |
| الوصول الحر: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab341ced933b9f4d02279194963d0e8bTest
https://doi.org/10.1002/uog.24810Test |
| حقوق: |
OPEN |
| رقم الانضمام: |
edsair.doi.dedup.....ab341ced933b9f4d02279194963d0e8b |
| قاعدة البيانات: |
OpenAIRE |
