Heatmap‐Guided Attention Networks for Automatic Segmentation in Lung Computed Tomography Images

ABSTRACT

In this paper, we propose an automatic image segmentation (AIS) framework for medical imaging that integrates a heatmap attention mechanism (HMAM) into two deep neural network (DNN) architectures: U‐Net and generative adversarial networks (GANs). This HMAM guides the segmentation process by incorporating targeted spatial information that directs the networks toward clinically relevant regions. Heatmaps are generated using a modified VGG16 network, and the learned filters are subsequently transferred to conventional DNNs, whose activations provide complementary spatial guidance during segmentation. Furthermore, we develop two GAN‐based segmentation models that employ a U‐Net architecture and a U‐shaped modified VGG16 architecture as generator networks. In these models, the HMAM is incorporated exclusively into the generator network (GN), rather than the discriminator network (DN), because the GN directly produces the pixel‐wise segmentation mask and is therefore expected to benefit more from spatial guidance. The proposed methods are evaluated using COVID‐19 lung computed tomography (CT) images. Experimental results demonstrate consistent improvements over matched conventional baselines, particularly under limited‐data conditions. For example, the mean dice score of the U‐Net architecture increased from 61.05% to 70.49% on the larger data split and from 73.80% to 84.12% on the smaller split. Similarly, the proposed GAN‐based models achieved substantial performance gains, with the mean dice score increasing from 55.87% to 67.90% for the U‐Net‐based generator and from 58.11% to 78.61% for the VGG16‐based generator.