When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation. Meta-UNet can use a small amount of annotated image data to complete the task of MR image segmentation and obtain good segmentation results. Meta-UNet improves U-Net by introducing dilated convolution, which can increase the receptive field of the model to improve the sensitivity to targets of different scales. We introduce the attention mechanism to improve the adaptability of the model to different scales. We introduce the meta-learning mechanism, and employ a composite loss function for well-supervised and effective bootstrapping of model training. We use the proposed Meta-UNet model to train on different segmentation tasks, and then use the trained model to evaluate on a new segmentation task, where the Meta-UNet model achieves high-precision segmentation of target images. Meta-UNet has a certain improvement in mean Dice similarity coefficient (DSC) compared with voxel morph network (VoxelMorph), data augmentation using learned transformations (DataAug) and label transfer network (LT-Net). Experiments show that the proposed method can effectively perform MR image segmentation using a small number of samples. It provides a reliable aid for clinical diagnosis and treatment.
Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously. The model consists of a segmentation subnet and a classification subnet. A multi-scale feature fusion method is proposed in the classification subnet to improve the classification accuracy, and a boundary-aware attention is designed in the segmentation subnet to solve the problem of tumor over-segmentation. A dynamic weighted average multi-task loss is used to make the model achieve optimal performance in both tasks simultaneously. The experimental results of this method on 295 HCC patients are superior to other multi-task learning methods, with a Dice similarity coefficient (Dice) of (83.9 ± 0.88)% on the segmentation task, while the average recall is (86.08 ± 0.83)% and an F1 score is (80.05 ± 1.7)% on the classification task. The results show that the multi-task learning method proposed in this paper can perform the classification task and segmentation task well at the same time, which can provide theoretical reference for clinical diagnosis and treatment of HCC patients.
Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.
Objective To investigate the financial burden of in-patients with thyroid diseases in the West China Hospital in Chengdu, Sichuan province, from January 2011 to December 2012, so as to provide baseline data for further research. Methods The data of in-patients (who had been discharged from the department of endocrinology and metabolism or discharged after being transferred to other departments for diagnosis and treatment in the West China Hospital in 2011) were collected from the Hospital Information System (HIS) of the West China Hospital, including basic information, initial diagnosis when the patients were discharged, hospital costs, the information about whether the patients had been registered the insurance in hospital, etc. We classified diseases according to ICD-10 based on the initial diagnosis when the patients were discharged on the first page of case reports. The data were input using Excel 2010 software, and statistical analysis was performed using SPSS 13.0 software. Results The results showed that: a) in 2011, 205 person-times were hospitalized in the department of endocrinology and metabolism, of which, 84 were male and 121 were female, with mean age of 45.3±15.7 years; b) for patients with thyroid diseases, median hospital stay was 10 days, the average cost of hospital stay for each patient was RMB 2 881.43 yuan, most of which was for lab tests and examination; c) the person-times of patients with hyperthyroidism was 162, accounting for 79.5% of the total of thyroid diseases, median hospital stay was 10 days, and the average cost of hospital stay was RMB 2 958.36 yuan; and d) there was no association between the number of hyperthyroidism complications and hospital stay and costs. Conclusion Thyroid diseases are a commonly-seen disease in the department of endocrinology and metabolism, of which, hyperthyroidism accounts for the most. There is no association between the number of hyperthyroidism complications and hospital stay/costs.