Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images

The proposed pipeline offers a precise and reliable alternative to conventional manual measurements for GS segmentation and GA estimation. Furthermore, its potential extends to segmenting and measuring other fetal components in future studies.

Authors

Institutions

Publication Details

Journal
Frontiers in Pediatrics
Published
2024-12-18
DOI
https://doi.org/10.3389/fped.2024.1453302
Citations
5
Primary Topic
Cleft Lip and Palate Research
Type
article
Field-Weighted Citation Impact
2.75
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images

Zobia Suhail, Hafiz Muhammad Danish, Faiza Farooq
5 citations
Frontiers in Pediatrics
Cleft Lip and Palate Research
2.75
article

Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images

Zobia Suhail, Hafiz Muhammad Danish, Faiza Farooq
article en
5 citations

Abstract

The proposed pipeline offers a precise and reliable alternative to conventional manual measurements for GS segmentation and GA estimation. Furthermore, its potential extends to segmenting and measuring other fetal components in future studies.

Frontiers in PediatricsVol. 12
University of Lahore (PK), University of the Punjab (PK)
Openalex Percentile: Top 9%
Cleft Lip and Palate Research
2.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.