Lung cancer is the most common tumor and one of the malignant tumors with the lowest livability after diagnosis, as is known so far. Large-scale image database is the foundation of developing computer-aided diagnosis methods, education and training in lung cancer diagnosis to improve medical diagnostic efficiency and to reduce the doctors' burden. In this study, aiming at improving the low data storage efficiency and solving the lacking of tool for data visualization and data retrieval existing in the use of traditional Lung Image Database Consortium (LIDC) from the lung cancer database, we developed a new lung cancer image database platform including an improved data model, a data integration tool, an image and annotation visualization tool and a data retrieving component. Firstly, the data format in LIDC was analyzed and an improved information model was provided to manage and manipulate large amount data stored in it. Next, some tools such as data integration component, DICOM, image and annotation visualization tool, and data query were designed and implemented. The study demonstrated that the lung cancer image database platform had the capacity of data collection, visualization, and query, and could promote diagnose lung cancer research.
Algorithms ; Databases, Factual ; Diagnosis, Computer-Assisted ; Humans ; Information Storage and Retrieval ; methods ; Lung Neoplasms ; diagnostic imaging ; pathology ; Radiographic Image Interpretation, Computer-Assisted ; methods ; Tomography, X-Ray Computed ; methods ; standards

Objective To explore the diagnostic value of qualitative and quantitative analysis for wrist and hand changes of rheumatoid arthritis (RA) patients based on 3.0T MR images.Methods A total of 39 RA patients were enrolled and divided into 2 groups according to the course of the diseases,including 20 cases defined as early stage group (≤24 months) and 19 cases defined as middle-late stage group (＞24 months).MRI features such as joint synovitis,bone marrow edema,bone erosion,the tenosynovitis in wrists and hands were observed emphatically.Volumes of synovitis and bone marrow edema for all patients were quantified with the software developed by Kunming University of Science and Technology.Results Among the 78 sides of hands and wrists in 39 patients,the incidence rate of synovitis,bone marrow edema,bone erosion,tenosynovitis was 94.87％ (37/39),64.10％ (25/39),61.54％ (24/39) and 76.92％ (30/39),respectively.The highest incidence rate of synovitis,bone marrow edema and bone erosion was respectively found in the wrist (72/78,92.31 ％),carpus (48/78,61.54 ％) andtriangular bone (50/78,64.1 ％).There was no statistical significance difference of the occurrence of peritendinitis between the flexor tendons (74.36％ [58/78]) and extensor tendons (61.54％ [48/78];x2 =2.94,P=0.09).No statistical difference of the incidence rates of synovitis,bone marrow edema,bone erosion and tenosynovitis was found between the early stage group and middle-late stage group (all P＞0.05).There was no significant difference of synovitis and bone marrow edema volumes between the early stage group and the middle-late stage group (both P＞0.05) with MRI quantitative analysis.Conclusion 3.0T MRI can clearly demonstrate the pathological changes of the wrists and hands in RA patients.The quantitative analysis software can provide more accurate indicators for the assessment of disease severity.

Cutaneous malignant melanoma is a common malignant tumor. Accurate segmentation of the lesion area is extremely important for early diagnosis of the disease. In order to achieve more effective and accurate segmentation of skin lesions, a parallel network architecture based on Transformer is proposed in this paper. This network is composed of two parallel branches: the former is the newly constructed multiple residual frequency channel attention network (MFC), and the latter is the visual transformer network (ViT). First, in the MFC network branch, the multiple residual module and the frequency channel attention module (FCA) module are fused to improve the robustness of the network and enhance the capability of extracting image detailed features. Second, in the ViT network branch, multiple head self-attention (MSA) in Transformer is used to preserve the global features of the image. Finally, the feature information extracted from the two branches are combined in parallel to realize image segmentation more effectively. To verify the proposed algorithm, we conducted experiments on the dermoscopy image dataset published by the International Skin Imaging Collaboration (ISIC) in 2018. The results show that the intersection-over-union (IoU) and Dice coefficients of the proposed algorithm achieve 90.15% and 94.82%, respectively, which are better than the latest skin melanoma segmentation networks. Therefore, the proposed network can better segment the lesion area and provide dermatologists with more accurate lesion data.
Humans ; Dermoscopy/methods* ; Neural Networks, Computer ; Melanoma/pathology* ; Skin Neoplasms/pathology* ; Image Processing, Computer-Assisted/methods*