Research Article
A Generalization Study of Automatic Pericardial Segmentation in Computed Tomography Images
@INPROCEEDINGS{10.1007/978-3-031-32029-3_15, author={R\^{u}ben Baeza and Carolina Santos and F\^{a}bio Nunes and Jennifer Mancio and Ricardo Fontes-Carvalho and Miguel Coimbra and Francesco Renna and Jo\"{a}o Pedrosa}, title={A Generalization Study of Automatic Pericardial Segmentation in Computed Tomography Images}, proceedings={Wireless Mobile Communication and Healthcare. 11th EAI International Conference, MobiHealth 2022, Virtual Event, November 30 -- December 2, 2022, Proceedings}, proceedings_a={MOBIHEALTH}, year={2023}, month={5}, keywords={Pericardium Segmentation Deep Learning U-Net Computed Tomography}, doi={10.1007/978-3-031-32029-3_15} }- Rúben Baeza
Carolina Santos
Fábio Nunes
Jennifer Mancio
Ricardo Fontes-Carvalho
Miguel Coimbra
Francesco Renna
João Pedrosa
Year: 2023
A Generalization Study of Automatic Pericardial Segmentation in Computed Tomography Images
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-32029-3_15
Abstract
The pericardium is a thin membrane sac that covers the heart. As such, the segmentation of the pericardium in computed tomography (CT) can have several clinical applications, namely as a preprocessing step for extraction of different clinical parameters. However, manual segmentation of the pericardium can be challenging, time-consuming and subject to observer variability, which has motivated the development of automatic pericardial segmentation methods.
In this study, a method to automatically segment the pericardium in CT using a U-Net framework is proposed. Two datasets were used in this study: the publicly available Cardiac Fat dataset and a private dataset acquired at the hospital centre of Vila Nova de Gaia e Espinho (CHVNGE).
The Cardiac Fat database was used for training with two different input sizes - 512(\times )512 and 256(\times )256. A superior performance was obtained with the 256(\times )256 image size, with a mean Dice similarity score (DCS) of 0.871 ± 0.01 and 0.807 ± 0.06 on the Cardiac Fat test set and the CHVNGE dataset, respectively.
Results show that reasonable performance can be achieved with a small number of patients for training and an off-the-shelf framework, with only a small decrease in performance in an external dataset. Nevertheless, additional data will increase the robustness of this approach for difficult cases and future approaches must focus on the integration of 3D information for a more accurate segmentation of the lower pericardium.
