145 related articles for article (PubMed ID: 30440496)
1. Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks.
Sinclair M; Baumgartner CF; Matthew J; Bai W; Martinez JC; Li Y; Smith S; Knight CL; Kainz B; Hajnal J; King AP; Rueckert D
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():714-717. PubMed ID: 30440496
[TBL] [Abstract][Full Text] [Related]
2. A new approach to automatic measure fetal head circumference in ultrasound images using convolutional neural networks.
Yang C; Yang Z; Liao S; Guo J; Yin S; Liu C; Kang Y
Comput Biol Med; 2022 Aug; 147():105801. PubMed ID: 35785663
[TBL] [Abstract][Full Text] [Related]
3. Scientific basis for standardization of fetal head measurements by ultrasound: a reproducibility study.
Napolitano R; Donadono V; Ohuma EO; Knight CL; Wanyonyi SZ; Kemp B; Norris T; Papageorghiou AT
Ultrasound Obstet Gynecol; 2016 Jul; 48(1):80-5. PubMed ID: 27158767
[TBL] [Abstract][Full Text] [Related]
4. Automatic fetal biometry prediction using a novel deep convolutional network architecture.
Ghelich Oghli M; Shabanzadeh A; Moradi S; Sirjani N; Gerami R; Ghaderi P; Sanei Taheri M; Shiri I; Arabi H; Zaidi H
Phys Med; 2021 Aug; 88():127-137. PubMed ID: 34242884
[TBL] [Abstract][Full Text] [Related]
5. Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning.
Sobhaninia Z; Rafiei S; Emami A; Karimi N; Najarian K; Samavi S; Reza Soroushmehr SM
Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():6545-6548. PubMed ID: 31947341
[TBL] [Abstract][Full Text] [Related]
6. Efficient fetal ultrasound image segmentation for automatic head circumference measurement using a lightweight deep convolutional neural network.
Zeng W; Luo J; Cheng J; Lu Y
Med Phys; 2022 Aug; 49(8):5081-5092. PubMed ID: 35536111
[TBL] [Abstract][Full Text] [Related]
7. Fetal biometry: a comparison between experienced sonographers and automated measurements.
Zalud I; Good S; Carneiro G; Georgescu B; Aoki K; Green L; Shahrestani F; Okumura R
J Matern Fetal Neonatal Med; 2009 Jan; 22(1):43-50. PubMed ID: 19165678
[TBL] [Abstract][Full Text] [Related]
8. Automatic fetal head measurements from sonographic images.
Chalana V; Winter TC; Cyr DR; Haynor DR; Kim Y
Acad Radiol; 1996 Aug; 3(8):628-35. PubMed ID: 8796726
[TBL] [Abstract][Full Text] [Related]
9. Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images.
Li P; Zhao H; Liu P; Cao F
Med Biol Eng Comput; 2020 Nov; 58(11):2879-2892. PubMed ID: 32975706
[TBL] [Abstract][Full Text] [Related]
10. Finding the most accurate method to measure head circumference for fetal weight estimation.
Schmidt U; Temerinac D; Bildstein K; Tuschy B; Mayer J; Sütterlin M; Siemer J; Kehl S
Eur J Obstet Gynecol Reprod Biol; 2014 Jul; 178():153-6. PubMed ID: 24802187
[TBL] [Abstract][Full Text] [Related]
11. Automated fetal head detection and measurement in ultrasound images by iterative randomized Hough transform.
Lu W; Tan J; Floyd R
Ultrasound Med Biol; 2005 Jul; 31(7):929-36. PubMed ID: 15972198
[TBL] [Abstract][Full Text] [Related]
12. Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2.
Liang B; Peng F; Luo D; Zeng Q; Wen H; Zheng B; Zou Z; An L; Wen H; Wen X; Liao Y; Yuan Y; Li S
BMC Med Inform Decis Mak; 2024 May; 24(1):128. PubMed ID: 38773456
[TBL] [Abstract][Full Text] [Related]
13. Artificial intelligence assistance for fetal head biometry: Assessment of automated measurement software.
Ambroise Grandjean G; Hossu G; Bertholdt C; Noble P; Morel O; Grangé G
Diagn Interv Imaging; 2018 Nov; 99(11):709-716. PubMed ID: 30177447
[TBL] [Abstract][Full Text] [Related]
14. Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net.
Zeng Y; Tsui PH; Wu W; Zhou Z; Wu S
J Digit Imaging; 2021 Feb; 34(1):134-148. PubMed ID: 33483862
[TBL] [Abstract][Full Text] [Related]
15. Automatic evaluation of fetal head biometry from ultrasound images using machine learning.
Kim HP; Lee SM; Kwon JY; Park Y; Kim KC; Seo JK
Physiol Meas; 2019 Jul; 40(6):065009. PubMed ID: 31091515
[TBL] [Abstract][Full Text] [Related]
16. Discrepancy in ultrasound biometric parameters of the head (HC--head circumference, BPD--biparietal diameter) in breech presented fetuses.
Lubusky M; Prochazka M; Langova M; Vomackova K; Cizek L
Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub; 2007 Dec; 151(2):323-6. PubMed ID: 18345272
[TBL] [Abstract][Full Text] [Related]
17. Deep learning fetal ultrasound video model match human observers in biometric measurements.
Płotka S; Klasa A; Lisowska A; Seliga-Siwecka J; Lipa M; Trzciński T; Sitek A
Phys Med Biol; 2022 Feb; 67(4):. PubMed ID: 35051921
[No Abstract] [Full Text] [Related]
18. Automated measurement of fetal head circumference using 2D ultrasound images.
van den Heuvel TLA; de Bruijn D; de Korte CL; Ginneken BV
PLoS One; 2018; 13(8):e0200412. PubMed ID: 30138319
[TBL] [Abstract][Full Text] [Related]
19. Interactive automatic fetal head measurements from ultrasound images using multimedia computer technology.
Pathak SD; Chalana V; Kim Y
Ultrasound Med Biol; 1997; 23(5):665-73. PubMed ID: 9253814
[TBL] [Abstract][Full Text] [Related]
20. An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation.
Prieto JC; Shah H; Rosenbaum AJ; Jiang X; Musonda P; Price JT; Stringer EM; Vwalika B; Stamilio DM; Stringer JSA
Proc SPIE Int Soc Opt Eng; 2021 Feb; 11596():. PubMed ID: 33935344
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]