Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy

Marta Saiz-Vivó¹  Adrián Colomer¹  Valery Naranjo¹  Luis Martí-Bonmatí²  Carles Fonfría² 

[1] Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain;Radiology Department, La Fe University and Polytechnic Hospital, 46026 Valencia, Spain;

关键词: supervised domain adaptation;    MRI sequences;    atrial geometry;    semantic segmentation;   

DOI  :  10.3390/e23070898

来源: DOAJ

【 摘 要 】

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

【 授权许可】

Unknown