000 | 01417 am a22002653u 4500 | ||
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042 | _adc | ||
100 | 1 | 0 |
_aYasrab, Robail _eauthor _92275 |
700 | 1 | 0 |
_aFu, Zeyu _eauthor _92276 |
700 | 1 | 0 |
_aDrukker, Lior _eauthor |
700 | 1 | 0 |
_aLee, Lok Hin _eauthor |
700 | 1 | 0 |
_aZhao, He _eauthor |
700 | 1 | 0 |
_aPapageorghiou, Aris T. _eauthor |
700 | 1 | 0 |
_aNoble, Alison J. _eauthor |
245 | 0 | 0 | _aEnd-to-end First Trimester Fetal Ultrasound Video Automated CRL and NT Segmentation |
260 | _c2022-04-28. | ||
500 | _a/pmc/articles/PMC7614066/ | ||
500 | _a/pubmed/36643819 | ||
520 | _aThis study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations. | ||
540 | _a | ||
546 | _aen | ||
690 | _aArticle | ||
655 | 7 |
_aText _2local |
|
786 | 0 | _nProc IEEE Int Symp Biomed Imaging | |
856 | 4 | 1 |
_uhttp://dx.doi.org/10.1109/ISBI52829.2022.9761400 _zConnect to this object online. |
999 |
_c2224 _d2224 |