Abstract

Objective Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is proposed to automatically segment venous thrombus with high accuracy and reliability. Methods In order to train, test, and external test the developed network, total images of 110 subjects are obtained from three different centers with two different black-blood MR techniques (i.e., DANTE-SPACE and DANTE-FLASH). Two experienced radiologists manually contoured each venous thrombus, followed by reediting, to create the ground truth. 5-fold cross-validation strategy is applied for training and testing. The segmentation performance is measured on pixel and vessel segment levels. For the pixel level, the dice similarity coefficient (DSC), average Hausdorff distance (AHD), and absolute volume difference (AVD) of segmented thrombus are calculated. For the vessel segment level, the sensitivity (SE), specificity (SP), accuracy (ACC), and positive and negative predictive values (PPV and NPV) are used. Results The proposed network generates segmentation results in good agreement with the ground truth. Based on the pixel level, the proposed network achieves excellent results on testing and the other two external testing sets, DSC are 0.76, 0.76, and 0.73, AHD (mm) are 4.11, 6.45, and 6.49, and AVD are 0.16, 0.18, and 0.22. On the vessel segment level, SE are 0.95, 0.93, and 0.81, SP are 0.97, 0.92, and 0.97, ACC are 0.96, 0.94, and 0.95, PPV are 0.97, 0.82, and 0.96, and NPV are 0.97, 0.96, and 0.94. Conclusions The proposed deep learning network is effective and stable for fully automatic segmentation of venous thrombus on black blood MR images.

Highlights

  • Deep vein thrombosis (DVT) is the third-largest cardiovascular disease after cerebral vascular and coronary artery disease, it occurs mainly in the lower extremities, and with the acceleration of population aging, the incidence rate of DVT is increasing year by year [1]

  • We develop a fully automatic method of venous thrombus segmentation based on deep learning network and blood thrombus imaging (BTI) images, aiming to reduce the burden of clinicians and improve the efficiency and accuracy of DVT personalized treatment planning

  • Since 3D deep learning networks have demonstrated their superiority on volumetric medical image segmentation task [24, 25], the developed network is only compared with these state-of-the-art 3D deep learning-based models

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Summary

Introduction

Deep vein thrombosis (DVT) is the third-largest cardiovascular disease after cerebral vascular and coronary artery disease, it occurs mainly in the lower extremities, and with the acceleration of population aging, the incidence rate of DVT is increasing year by year [1]. Failure to accurately diagnose DVT can lead to severe complications, such as postthrombotic syndrome, pulmonary embolism, lower extremity venous ulcer, and chronic pulmonary hypertension [2]. An MR black-blood thrombus imaging (BTI) technique was developed to diagnose DVT [3]. The technique uses a black-blood preparation to suppress the venous blood flow signals and make the thrombus be directly visualized within the black-blood venous lumen. Some studies have demonstrated that BTI is reliable and accurate for diagnosing DVT without the use of contrast agents [4, 5]. Accurate quantification of thrombus characteristics, such as thrombus distribution, signal intensities, volume, and shape, can provide additional information for personalized DVT treatment planning [6, 7]

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