Deep learning-based multimodal image analysis for cervical cancer detection

• Published in: Methods (San Diego, Calif.) Vol. 205; pp. 46 - 52
• Main Authors: Ming, Yue, Dong, Xiying, Zhao, Jihuai, Chen, Zefu, Wang, Hao, Wu, Nan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2022
• Subjects: Cervical cancer; Deep learning; Lesion detection; Multimodal image fusion; Deep learning; Multimodal image fusion; Lesion detection; Cervical cancer; lesion detection; deep learning; multimodal image fusion; cervical cancer
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2022.05.004

•A unified framework to detect cervical cancer using multimodal medical images.•An adaptive multi-model method to fuse multimodal medical images.•PET/CT fusion is more beneficial for the detection of cervical cancer than using either of them on their own; for example, only using CT mode detection or only using PET mode detection is not better than using PET/CT fusion mode detection. Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis prediction. Fluorodeoxyglucose positron emission tomography and computed tomography, i.e., FDG-PET/CT and PET/CT, have established roles with superior sensitivity and specificity in most cancer imaging applications. However, a typical FDG-PET/CT analysis involves the time-consuming process of interpreting hundreds of images, and the intense image screening work has greatly hindered clinicians. We propose a computer-aided deep learning-based framework to detect cervical cancer using multimodal medical images to increase the efficiency of clinical diagnosis. This framework has three components: image registration, multimodal image fusion, and lesion object detection. Compared to traditional approaches, our adaptive image fusion method fuses multimodal medical images. We discuss the performance of deep learning in each modality, and we conduct extensive experiments to compare the performance of different image fusion methods with some state-of-the-art (SOTA) object-detection deep learning-based methods in images with different modalities. Compared with PET, which has the highest recognition accuracy in single-modality images, the recognition accuracy of our proposed method on multiple object detection models is improved by an average of 6.06%. And compared with the best results of other multimodal fusion methods, our results have an average improvement of 8.9%.