Guanghui Han

Digital core refers to the use of three-dimensional physical imaging equipment and mathematical modeling to image the internal microstructure of a core and the use of computers to study the connectivity and pore distribution of the core microstructure. The digital core has emerged as a prominent research avenue in image processing in recent years. A three-dimensional (3D) image can be used to effectively study the microstructure and physical properties of a core. Three-dimensional reconstruction from two-dimensional core images is a crucial advancement in this field. Deep learning is advantageous in image reconstruction. However, when the traditional generative adversarial network (GAN) reconstruction method is adapted for reconstructing gray-scale core images, maintaining texture characteristics is difficult; additionally, it may produce blur artifacts in the GAN-generated gray-scale images. In this study, the physics statistical descriptor-informed deep image structure and texture similarity (PSDI-DISTS) metric, a higher-level metric than the traditional correlation function, is used as the loss function of the network, and a texture feature-constrained GAN (TFCGAN) model is proposed for reconstructing gray-scale core images. In addition, a balanced training strategy integrating L₁ and PSDI-DISTS losses is designed to optimize the model performance. The experimental results and seepage simulation analysis showed that TFCGAN can maintain the textural characteristics in the gray-scale core image reconstruction results. Furthermore, the reconstruction results exhibited seepage characteristics similar to those of the target.

Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images. In response to the above issues, we introduced a deep reinforcement learning (DRL)-based method to locate nasopharyngeal carcinoma lesions in 3D-CT scans. The proposed method uses prior knowledge to guide the agent to reasonably reduce the search space and promote the convergence rate of the model. Furthermore, the multi-scale processing technique is also used to promote the localization of small objects. We trained the proposed model with 3D-CT scans of 50 patients and evaluated it with 3D-CT scans of 30 patients. The experimental results showed that the proposed model has strong robustness, and its accuracy was improved by more than 1 mm on average under the premise of using a smaller dataset compared with the DQN models in recent studies. The proposed model could effectively locate the lesion area of nasopharyngeal carcinoma in 3D-CT scans.

Early detection and treatment of nasopharyngeal carcinoma has an important impact on improving the survival rate of patients. Computer-aided detection based on deep learning methods can automatically detect the presence of nasopharyngeal carcinoma on patient magnetic resonance images (MRI), assisting in the assessment of tumor progression. However, large-scale annotation of MRI images is not feasible because it is time-consuming and burdens the healthcare system. This paper proposes a weakly supervised nasopharyngeal carcinoma detection method suitable for MRI images, which can obtain better detection performance with a small amount of labeled data. We first generate a pseudo-color version of the MRI image based on a multi-window sampling method, which preserves richer information and improves the information utilization of the image. Then, active learning and deep learning are combined to construct an active detection model for nasopharyngeal cancer, and the most representative image set is selected from a large-scale unlabeled set by using instance-level image uncertainty for further annotation by experts, which significantly reduces the demand for image annotation in deep network. The proposed method is verified on the MRI image set of 800 patients with nasopharyngeal carcinoma. The experimental results show that the resampling method based on multi-window settings can improve the performance of the classical depth detection model by 1.5 %, while the active detection model of nasopharyngeal carcinoma only uses 20 % labeled data to achieve 92.6 % of the performance of the deep learning detector trained with all samples. Good performance is obtained when the label set is small. Our active detection method for nasopharyngeal carcinoma can detect the lesion area of nasopharyngeal carcinoma with high accuracy without large-scale labeled data, which significantly reduces the sample labeling burden of doctors.

Nasopharyngeal carcinoma (NPC) is a malignant tumor, and early diagnosis and timely treatment are important for NPC patients. Accurate and reliable detection of NPC lesions in magnetic resonance (MR) images is very helpful for the disease diagnosis. However, recent deep learning methods need to be improved for NPC detection in MR images. Because NPC tumors are invasive and usually small in size, it is difficult to distinguish NPC tumors from the closely connected surrounding tissues in a huge and complex background. In this paper, we propose an automatic detection method, named MWSR-YLCA, to accurately detect NPC lesions in MR images. Specifically, we design two modules, the multi-window settings resampling (MWSR) module and an improved YOLOv7 embedded with a coordinate attention mechanism (YLCA) module, to detect NPC lesions more accurately. First, the MWSR generates a pseudo-color version of MR images based on a multi-window resampling method, which preserves richer information. Subsequently, the YLCA detects the NPC lesion areas more accurately by constructing a novel network based on an improved YOLOv7 framework embedded with the coordinate attention mechanism. The proposed method was validated on an MR image set of 800 NPC patients and obtained 80.1% mAP detection performance with only 4694 data samples. The experimental results show that the proposed MWSR-YLCA method can perform high-accuracy detection of NPC lesions and has superior performance.

Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models.

Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher–student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.

Radiotherapy is an essential method for treating nasopharyngeal carcinoma (NPC), and the segmentation of NPC is a crucial process affecting the treatment. However, manual segmentation of NPC is inefficient. Besides, the segmentation results of different doctors might vary considerably. To improve the efficiency and the consistency of NPC segmentation, we propose a novel AttR2U-Net model which automatically and accurately segments nasopharyngeal carcinoma from MRI images. This model is based on the classic U-Net and incorporates advanced mechanisms such as spatial attention, residual connection, recurrent convolution, and normalization to improve the segmentation performance. Our model features recurrent convolution and residual connections in each layer to improve its ability to extract details. Moreover, spatial attention is fused into the network by skip connections to pinpoint cancer areas more accurately. Our model achieves a DSC value of 0.816 on the NPC segmentation task and obtains the best performance compared with six other state-of-the-art image segmentation models.

Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractable, and the performance of current models is mostly unsatisfactory with indistinguishable and blurred boundaries of segmentation results of tiny tumor volume. To address the problem, we propose a densely connected deep convolutional network consisting of an encoder network and a corresponding decoder network, which extracts high-level semantic features from different levels and uses low-level spatial features concurrently to obtain fine-grained segmented masks. Skip-connection architecture is involved and modified to propagate spatial information to the decoder network. Preliminary experiments are conducted on 30 patients. Experimental results show our model outperforms all baseline models, with improvements of 4.17%. An ablation study is performed, and the effectiveness of the novel loss function is validated.