A Coarse-to-Fine Detection Framework for Automated Lung Tumour Detection From 3D PET/CT Images

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection is critical to improving treatment outcomes and survival rates. Positron emission tomography/computed tomography (PET/CT) is a widely used imaging modality for identifying lung tumours. However, limitations in imaging resolution and the complexity of cancer characteristics make detecting small lesions particularly challenging. To address this issue, we propose a novel coarse-to-fine detection framework to reduce missed diagnoses of small lung lesions in PET/CT images. Our method integrates a stacked detection structure with a multi-attention guidance mechanism, effectively leveraging spatial and contextual information from small lesions to enhance lesion localisation. Experimental evaluations on a PET/CT dataset of 225 patients demonstrate the effectiveness of our method, achieving remarkable results with a precision of 81.74%, a recall of 76.64%, and an mAP of 84.72%. The proposed framework not only improves the detection accuracy of small target lesions in the lung but also provides a more reliable solution for early diagnosis.