Adaptive EfficientNet-DeeplabV3+ for Left Ventricular Wall Segmentation

Myocardial infarction (MI) is usually diagnosed by various methods, such as cardiac imaging modalities, by observing the structure and movement of the left ventricular (LV) wall. However, left ventricular ultrasound images often suffer from noise and poor image quality. This challenge can adapt segmentation methods on medical images to help health professionals observe the LV wall more clearly. This research segmented the LV wall using a deep learning architecture that adaptively adjusts the width and depth of the network based on the input resolution, called Adaptive EfficientNet-DeepLabV3+. The modification involves replacing the DeepLabV3+encoder with EfficientNet and incorporating a scaling function to adjust the depth and width of the network based on a given resolution, using scaling coefficients in a scaling function that increases or decreases these parameters equally. To evaluate its performance, it compares the architecture's adaptability with a standard DeepLabV3+model, using publicly available datasets provided by Hamad Medical Corporation, Qatar University, and Tampere University (HMC-QU) with variations in input resolution. The test results show that Adaptive EfficientNet-Deep-LabV3+ can adaptively adjust the depth and width of the network, as indicated by changes in the number of model parameters based on the input resolution while maintaining high accuracy, reaching 0.99225, and achieving an intersection over onion (IOU) of 0.80908 on test data.