  Nowadays, cardiac diagnosis largely depends on left ventricular function
assessment. With the help of the segmentation deep learning model, the
assessment of the left ventricle becomes more accessible and accurate. However,
deep learning technique still faces two main obstacles: the difficulty in
acquiring sufficient training data and time-consuming in developing quality
models. In the ordinary data acquisition process, the dataset was selected
randomly from a large pool of unlabeled images for labeling, leading to massive
labor time to annotate those images. Besides that, hand-designed model
development is strenuous and also costly. This paper introduces a pipeline that
relies on Active Learning to ease the labeling work and utilizes Neural
Architecture Search's idea to design the adequate deep learning model
automatically. We called this Fully automated machine learning pipeline for
echocardiogram segmentation. The experiment results show that our method
obtained the same IOU accuracy with only two-fifths of the original training
dataset, and the searched model got the same accuracy as the hand-designed
model given the same training dataset.
