Aortic Dissection Diagnosis Based on Sequence Information and Deep Learning

Aortic dissection(AD)is one of the most serious diseases with high mortality,and its diagnosis mainly depends on computed tomography(CT)results.Most existing automatic diagnosis methods of AD are only suitable for AD recognition,which usually require preselection of CT images and cannot be further classified to different types.In this work,we constructed a dataset of 105 cases with a total of 49021 slices,including 31043 slices expertlevel annotation and proposed a two-stage AD diagnosis structure based on sequence information and deep learning.The proposed region of interest(RoI)extraction algorithm based on sequence information(RESI)can realize high-precision for RoI identification in the first stage.Then DenseNet-121 is applied for further diagnosis.Specially,the proposed method can judge the type of AD without preselection of CT images.The experimental results show that the accuracy of Stanford typing classification of AD is 89.19%,and the accuracy at the slice-level reaches 97.41%,which outperform the state-ofart methods.It can provide important decision-making information for the determination of further surgical treatment plan for patients.

Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

At present,segmentation for medical image is mainly based on fully supervised model training,which consumes a lot of time and labor for dataset labeling.To address this issue,we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries.The network is mainly composed of two parts:a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results.In the initial stage of network training,a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination capabilities.Then a semi-supervised method is adopted to train the model,in which the discriminant network will generate pseudo-labels on the results of the segmentation for semi-supervised training of the segmentation network.The proposed method can use a small part of annotated dataset to realize the segmentation of medical images and effectively solve the problem of insufficient medical image annotation data.