Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound
This work addresses the challenge of time-consuming and observer-dependent manual segmentation in medical imaging for cardiovascular diagnosis, representing an incremental improvement in domain adaptation.
The paper tackled the problem of automating left atrium segmentation in 3D ultrasound images to predict cardiovascular conditions, and the result showed that incorporating a shape prior and adversarial learning increased segmentation accuracy.
Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions. Automation of this process in 3D Ultrasound image data is desirable, since manual delineations are time-consuming, challenging and observer-dependent. Convolutional neural networks have made improvements in computer vision and in medical image analysis. They have successfully been applied to segmentation tasks and were extended to work on volumetric data. In this paper we introduce a combined deep-learning based approach on volumetric segmentation in Ultrasound acquisitions with incorporation of prior knowledge about left atrial shape and imaging device. The results show, that including a shape prior helps the domain adaptation and the accuracy of segmentation is further increased with adversarial learning.