PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans

分割 计算机科学 棱锥(几何) 2019年冠状病毒病(COVID-19) 编码器 人工智能 模式识别(心理学) 计算机视觉 医学 疾病 数学 病理 几何学 操作系统 传染病(医学专业)
作者
Fares Bougourzi,Cosimo Distante,Fadi Dornaika,Abdelmalik Taleb-Ahmed
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:86: 102797-102797 被引量:34
标识
DOI:10.1016/j.media.2023.102797
摘要

Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose, detect, and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. To improve the performance of the Att-Unet architecture and maximize the use of the Attention Gate, we propose the PAtt-Unet and DAtt-Unet architectures. PAtt-Unet aims to exploit the input pyramids to preserve the spatial awareness in all of the encoder layers. On the other hand, DAtt-Unet is designed to guide the segmentation of Covid-19 infection inside the lung lobes. We also propose to combine these two architectures into a single one, which we refer to as PDAtt-Unet. To overcome the blurry boundary pixels segmentation of Covid-19 infection, we propose a hybrid loss function. The proposed architectures were tested on four datasets with two evaluation scenarios (intra and cross datasets). Experimental results showed that both PAtt-Unet and DAtt-Unet improve the performance of Att-Unet in segmenting Covid-19 infections. Moreover, the combination architecture PDAtt-Unet led to further improvement. To Compare with other methods, three baseline segmentation architectures (Unet, Unet++, and Att-Unet) and three state-of-the-art architectures (InfNet, SCOATNet, and nCoVSegNet) were tested. The comparison showed the superiority of the proposed PDAtt-Unet trained with the proposed hybrid loss (PDEAtt-Unet) over all other methods. Moreover, PDEAtt-Unet is able to overcome various challenges in segmenting Covid-19 infections in four datasets and two evaluation scenarios.
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