Automatic Remasking of Digital Subtraction Angiography Images in Pulmonary Angiography

Mizukuchi, Takashi; Uemura, Takeshi; Kondo, Satoru; Abe, Shinji; Adachi, Shiro; Okumura, Naoki; Kondo, Takahisa; Koyama, Shuji

doi:10.1007/s10278-019-00270-8

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…When it is suspected that the blood flow of AVF is reduced in MHD patients, examination and diagnosis and treatment must be performed as early as possible, and digital subtraction angiography (DSA) is an important means to understanding the vascular function of AVF [7]. DSA is a digital subtraction technique based on sequential images, which obtains the difference part by subtracting two frames of images in the same part of the human body, eliminates bone and soft tissue structures, makes the vessels filled with contrast agent in the second frame appear in the subtraction map, and enhances the contrast [8][9][10]. DSA is of great significance in the clinical diagnosis of vascular diseases.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning‐Based Digital Subtraction Angiography Characteristics in Nursing of Maintenance Hemodialysis Patients

2022

Contrast Media & Molecular Imaging

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This study is aimed at exploring the diagnostic value of digital subtraction angiography (DSA) based on faster region-based convolutional networks (Faster-RCNN) deep learning for maintenance hemodialysis (MHD) diseases and to provide a theoretical basis for clinical nursing. A total of 50 MHD patients who were clinically diagnosed in the Blood Purification Center were randomly divided into the control group and the experimental group (25 cases for each group). The control group was given routine nursing intervention, and the experimental group was given overall nursing intervention under the supervision of DSA. A faster RCNN multitarget detection network was constructed to analyze the average accuracy of various vascular structures in the test set. The self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were used to evaluate the degree of anxiety and depression. The urine volume before and after the operation, local hematoma after a puncture, the incidence of complications, and nursing satisfaction were recorded. The results showed that the average accuracy of the vein, internal carotid artery, circle of Willis, venous sinus, and venous vessels was 0.876, 0.916, 0.994, 0.925, and 0.732, respectively. The success rate of surgery in the experiment group was higher than that in the control group, and the difference had statistical significance ( P < 0.05 ). The SAS score and SDS score in the experimental group were significantly lower than those in the control group ( P < 0.05 ). The total incidence rate of complications in the experimental group (16.00%) was significantly lower than that in the control group (44.00%) ( P < 0.05 ). The satisfaction rate of the experimental group was significantly higher than that of the control group ( P < 0.05 ). The Faster-RCNN model had the best effect in differentiating the circle of Willis and a poor effect in differentiating venous vessels. DSA based on Faster-RCNN can significantly improve the success rate of puncture in MHD patients. The implementation of holistic nursing intervention under its supervision can significantly reduce postoperative complications and improve patient satisfaction with nursing compared with routine nursing.

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Section: Introductionmentioning

confidence: 99%

Deep Learning‐Based Digital Subtraction Angiography Characteristics in Nursing of Maintenance Hemodialysis Patients

2022

Contrast Media & Molecular Imaging

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The Value of C-Arm Computed Tomography in Addition to Conventional Digital Subtraction Angiography in the Diagnostic Work-up of Patients with Suspected Chronic Thromboembolic Pulmonary Hypertension: An Update of 300 Patients

et al. 2022

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Automatic Remasking of Digital Subtraction Angiography Images in Pulmonary Angiography

Cited by 2 publications

References 8 publications

Deep Learning‐Based Digital Subtraction Angiography Characteristics in Nursing of Maintenance Hemodialysis Patients

Deep Learning‐Based Digital Subtraction Angiography Characteristics in Nursing of Maintenance Hemodialysis Patients

The Value of C-Arm Computed Tomography in Addition to Conventional Digital Subtraction Angiography in the Diagnostic Work-up of Patients with Suspected Chronic Thromboembolic Pulmonary Hypertension: An Update of 300 Patients

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