Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise of the echocardiographic images. Manual segmentation is subject to interobserver variability and is too slow for real-time image-guided interventions. We aim to develop a deep learning-based method for automated multi-structure segmentation of echocardiographic images.

中文翻译：

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使用 Cardiac-SegNet 进行超声心动图图像多结构分割


超声心动图图像的心脏边界分割对于心功能评估和疾病诊断具有重要意义。然而，由于超声心动图图像的低对比度和斑点噪声，分割心室具有挑战性。手动分割会受到观察者间差异的影响，并且对于实时图像引导干预来说速度太慢。我们的目标是开发一种基于深度学习的方法，用于超声心动图图像的自动多结构分割。