Automatic Segmentation Of Lumen And Media-Adventitia In IVUS Images

Intravascular ultrasound(IVUS)can accurately reflect the nature,severity and pathological composition of coronary heart disease,which has a large scanning radius and strong penetration ability.The delineation of lumen and media-adventitia borders in IVUS images is crucial for the quantitative analysis of coronary atherosclerotic plaque.It is quite time-consuming for clinical doctors to manually delineate lumen and MA borders due to that a IVUS pullback usually contains thousands of images Therefore,accurate and automatic detection of lumen and media-adventitia borders has important application value.However,the presence of ultrasonic shadows and complex anatomical structures(such as bifurcations,calcified plaques and fibrous plaques)challenges the automatic segmentation and detection of lumen and media-adventitia borders.Recently,the development of deep learning methods,especially convolutional neural network,provides a feasible method for the automatic segmentation of IVUS images.This thesis uses the method based on full convolution neural network to achieve the automatic segmentation of lumen and media-adventitia in IVUS images,and explores various factors which affect segmentation performance.The main work of this thesis is as follows(1)Conducting the IVUS image dataset.A total of 6,516 IVUS images from 175 pullbacks were acquired from different hospitals by different IVUS imaging catheters The IVUS images with bifurcation,adjacent vessels,and various shadow artifacts were included to reflect the general image characteristics in routine clinical acquisition.All images were manually delineated by the experienced IVUS image readers in the CardHemo laboratory and used as the ground truth.(2)A full convolution neural network(FCN)model based on RefineNet.The residual structure is introduced in the encoder layers of the developed model,and RefineNet model is used as the decoders of model.And,a certain number of frames of consecutive images are used as the input to the segmentation model,which can combine with the spatiotemporal information of the front and back frames to improve the segmentation results.This paper uses 4912 IVUS images as training set and 1,105 IVUS images as test set in experiment.The evaluation metrics,namely,Dice Similarity Coefficient(DSC),Jaccard Index(JI)and Hausdorff Distance(HD)are used to evaluate the segmentation performance of the models.For lumen border segmentation,DSC is 0.923(±0.059),JI is 0.908(±0.058)and HD is 0.339mm(±0.176mm).For media-adventitia border segmentation,DSC is 0.942(±0.048),JI is 0.932(±0.060)and HD is 0.372mm(±0.234mm).The results show that the proposed model can accurately segment the lumen and media-adventitia region of IVUS images,and less affected by shadow artifacts,various plaques,bifurcation and adjacent vessels(3)Research on the impact factors of model performance.This paper mainly explores three factors:(a)the ultrasonic imaging frequency of the IVUS images contained in the training dataset,(b)the number of training samples,(c)convolution network structures.The results exhibit that the performance of model trained with a mix of different ultrasonic imaging frequencies(40MHz and 60MHz)dataset is better than the model trained with single ultrasonic frequency dataset.The increase of the number of training samples has a certain impact on the overall segmentation performance of the model,especially on the segmentation results of IVUS images with bifurcation.Then,the model proposed in this paper is compared with the other four models,namely,U-net,DeepLapv3+,IVUS-net and the model proposed in Ref.[51].Experimental results show that the segmentation performance of the proposed model is equivalent to DeepLabv3+,and slightly better than the other three models.