Carotid Intima-Media Thickness Segmentation using Attention Mechanism based Convolutional Neural Network with Domain-Specific Objective Function

Abstract

CIMT (Carotid Intima-Media Thickness) has been proven to be both a significant and reliable marker for the evaluation of the risk of cardiovascular disease. Cardiovascular disease is the leading cause of mortality globally, and yet could be easily treated if detected in its early stages. A prime indicator of Cardiovascular disease, CIMT has previously been measured through manual examination of ultrasound videos for the gap between the Lumen-Intima and the Media-Adventitia interfaces, the two inner layers of the Carotid Artery. However, this method is not only inconvenient, but also time consuming. There has been a significant number of previous deep learning approaches to this issue, which have yielded substantial results. However, as this problem concerns the morality of patients directly, medical professionals have been hesitant to be dependent on these approaches, as the current accuracy of the state-of-the-art model still falls short to human observations. Furthermore, high performing models come at high computational costs. CIMT can actually be determined by a miniscule region of the Carotid Ultrasonic image, which many past researches have not taken into consideration. This paper proposes to use an attention mechanism to determine the region of interest and an encoder-decoder system which significantly reduces computational trade off while maintaining comparable accuracy. We also propose a novel connection loss to solve the disconnection problem in the prediction. The proposed model yields an unprecedented accuracy in terms of IoU and ACC of 0.78 and 0.99 respectively, substantially higher than previous state-of-the-art models by 18% and 8.8% on average.