2D/3D fetal cardiac dataset segmentation using a deformable model

Irving Dindoyal, Tryphon Lambrou, Jing Deng, Andrew Todd-Pokropek

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


To segment the fetal heart in order to facilitate the 3D assessment of the cardiac function and structure.
Ultrasound acquisition typically results in drop-out artifacts of the chamber walls. The authors outline a level set deformable model to automatically delineate the small fetal cardiac chambers. The level set is penalized from growing into an adjacent cardiac compartment using a novel collision detection term. The region based model allows simultaneous segmentation of all four cardiac chambers from a user defined seed point placed in each chamber.
The segmented boundaries are automatically penalized from intersecting at walls with signal dropout. Root mean square errors of the perpendicular distances between the algorithm's delineation and manual tracings are within 2 mm which is less than 10% of the length of a typical fetal heart. The ejection fractions were determined from the 3D datasets. We validate the algorithm using a physical phantom and obtain volumes that are comparable to those from physically determined means. The algorithm segments volumes with an error of within 13% as determined using a physical phantom.
Our original work in fetal cardiac segmentation compares automatic and manual tracings to a physical phantom and also measures inter observer variation.
Original languageEnglish
Pages (from-to)4338-4349
Number of pages12
JournalMedical Physics
Issue number7
Publication statusPublished - Jul 2011

Bibliographical note

cited By 5

This work was supported by EPSRC (GR/N14248/01) and MRC (D2025/31) under the Interdisciplinary Research Consortium scheme—“From Medical Images and Signals to Clinical Information” (MIAS IRC). Dr. Jing Deng is supported by MRC (G108/516).


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