Abstract
Segmentation of liver lesions on non-contrast magnetic resonance imaging (MRI) is critical for patient management and treatment planning. In clinical treatment, the imaging process suffers from high-risk, expensive, and time-consuming due to using contrast agents (CA). Furthermore, manual segmentation has the disadvantages of tedious, low-reproducibility, and high misdiagnosis rate. Although some deep-learning based works have attempted for liver lesions segmentation, they are all limited to the use of contrast-enhanced MRI. To avoid the limitations comes from CA, we proposed a Radiomics-guided Densely-UNet-Nested Generative Adversarial Networks (Radiomics-guided DUN-GAN) for automatic segmentation of liver lesions on non-contrast MRI. Radiomics-guided DUN-GAN includes a DUN segmentor and a Radiomics-guided discriminator. It uses radiomics feature of the multi-phase contrast image as prior knowledge to guide the extraction of key implicit contrast radiomics (ICR) features in non-contrast images, thus achieving the direct lesions segmentation without CA for the first time. In the DUN segmentor, an innovative nested structure of Densely-UNet-connection reliably completes the segmentation. The nested structure extracts global features, semantic features, and ICR features by reasonably sharing features and maximizing information flow. Those features are fused with a new direction strategy of multi-integration features to improve the segmentation ability. In the innovative Radiomics-guided discriminator, the radiomics feature combined with the semantic feature enhances the discrimination of Radiomics-guided discriminator. Moreover, it guides the segmentor for multiple feature extraction via using the adversarial mechanism. Radiomics-guided DUN-GAN learns the mapping relationship between images, extracting the key ICR in the non-contrast image, and finally completing the accurate segmentation. Radiomics-guided DUN-GAN obtained the Dice Similarity Coefficient results of 93.47± 0.83% for the segmentation of lesions in non-contrast images from 250 clinical subjects. The results verify the Radiomics-guided DUN-GAN is accurate and robust, and it has the possibility of becoming a safe, inexpensive, and time-saving medical assistant tool in clinical diagnosis.
Highlights
Segmentation of the liver lesions is essential for patient management and treatment planning. 1) The segmentation guides physician to make preoperative planning, which can effectively improve the success rate of lesions resectionThe associate editor coordinating the review of this manuscript and approving it for publication was Huazhu Fu .and the survival rate of patients [1]. 2) The quantitative assessment of segmentation helps physicians develop a treatment plan and predict patient survival. [2]. 3) The segmentation is considered comprehensively with the patient’s age, clinical symptoms and disease severity to guide the patient’s post-operative treatment to prevent further liver failure
EXPERIMENTAL RESULTS AND EVALUATION Radiomics-guided DUN-GAN is the first to complete the segmentation of liver lesions without contrast agents
The segmentation results can prove that our results outperform other methods, and the best result with a Dice Similarity Coefficient (DSC) of 93.47± 0.83 is obtained
Summary
Segmentation of the liver lesions is essential for patient management and treatment planning. 1) The segmentation guides physician to make preoperative planning, which can effectively improve the success rate of lesions resection. DEEP LEARNING-BASED METHODS Convolutional Neural Networks (CNNs), as one of the representatives of deep learning, has completely changed the natural image processing by utilizing its highly representative features of hierarchical learning( [27]–[29]), and have witnessed the successful application in the field of medical image analysis( [30]–[33]) Many researchers follow this trend and propose using various CNNs to learn the feature representation in liver and lesion segmentation applications. Innovatively nests two jump connection to form the DUN segmentor Such a nested connection can both maximize the flow of information and share more details during the segmentation process, stably and accurately extracting ICR features with adversarial function. Radiomics-guided fusion feature has stronger characterization ability, and can guide the work of the splitter through the confrontation mechanism(Fig. 4)
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