# DSA Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm

Authors: Jian Wang; Lin Ti; Xiaorui Sun; Ruping Yang; Nafei Zhang; Kejuan Sun
Journal: Scanning
 (2022)

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## Abstract

Objective. A deep learning algorithm was developed for automatic detection and localization of intracranial aneurysms in DSA, and its clinical characteristics were analyzed, and targeted nursing measures were formulated. Methods. Using a retrospective multicenter study method based on radiology reports, DSA images of aneurysms were randomly divided into 75 cases in the training set, 20 cases in the internal test set, and 35 cases in the external test set. Using a computer-aided detection method based on the three-dimensional U-Net (3D U-Net), after preprocessing DSA images, automatic segmentation of intracranial blood vessels is performed to obtain regions of interest, and based on the segmentation results, physicians’ annotations are introduced. The 3D U-Net network model is trained and adjusted, and the obtained model is used to automatically detect the cerebral aneurysm area. Results. Fivefold cross-validation was used for the training set and the internal test set, and a sensitivity of 94.4±1.1% was obtained. Automatic detection of aneurysms was performed on the external test set, and the average false positive rate was 0.86 FPs/case (false positives/case). The resulting sensitivity was 82.9%. The classification comparison of external test sets showed that the sensitivity of the method for detecting aneurysms with sizes of 5.00~<10.00 mm and ≥10.00 mm (88.2% and 100.0%) was higher than that for aneurysms with sizes of <3.00 mm and 3.00~<5.00 mm (50.0% and 72.7%). The sensitivity of patients aged 50-60 years and >60 years (90.0% and 87.5%) was higher than that of patients aged <50 years (66.7%), and there was little difference between different genders (84.6% in males and 81.8% in females). Conclusion. The deep learning algorithm has high diagnostic performance in detecting intracranial aneurysms, which is verified by external datasets.


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## Body

## 1. Introduction

An intracranial aneurysm (IA) is a tumor-like dilation caused by abnormal local cerebrovascular changes and is a relatively common cerebrovascular disease, with an incidence of about 3%~5% in the general population [1]. Most (about 90%) patients with unruptured intracranial aneurysms (UIAs) usually have no obvious clinical manifestations, and only about 10% of patients will have special manifestations such as headaches and unilateral facial numbness (an aneurysm that affects an adjacent nerve or brain structure) [2]. Rupture of IAs is the main cause of nontraumatic subarachnoid hemorrhage (SAH), with an annual incidence of about 1% [3, 4] but high mortality and disability rates [5–7]. About 12% of patients are diagnosed before treatment. They have died, and there is still a 40% mortality rate within one month after treatment. About 30% of patients will be left with neurological dysfunction, and only a small number of patients have a slightly better prognosis [8, 9]. Early accurate detection is of great significance to the clinical management and prognosis of patients with intracranial aneurysms.Medical imaging plays an increasingly important role in the diagnosis and treatment of diseases and is an important tool for doctors to carry out disease screening, clinical diagnosis, treatment guidance, and efficacy evaluation. In recent years, with the development of medical imaging technology, the detection rate of IAs is also increasing. Magnetic resonance angiography (MRA), computed tomography angiography (CTA), and digital subtraction angiography (DSA) are the common examination methods for IAs. At this stage, the “gold standard” for the diagnosis of IAs is still DSA, with a sensitivity of more than 95%, but the operation is more complicated, the equipment and technical requirements are high, and it has disadvantages such as difficult repetition, high price, invasiveness, and many complications. Clinical application is limited. However, due to the different shapes and sizes of IAs and the complex morphology of parent arteries, the workload of routine physical examination for rapid screening is large, and it is easy to miss diagnose and misdiagnose simply by relying on manual reading.Computer-aided diagnosis (CAD) is a branch of artificial intelligence (AI) technology, which uses the machine learning algorithm to analyze and judge medical images. Deep learning (DL) is currently the most promising machine learning algorithm and a key technology to revolutionize AI+ medical imaging (i.e., CAD). The DL method generally refers to a deep segmentation network model based on convolutional neural networks (CNN), which can automatically learn features layer by layer from a large number of input data and complete classification and recognition tasks, forming an end-to-end structure. It has strong robustness and generalization ability. The CAD system based on DL has great potential and clinical application value in improving the accuracy of pathological diagnosis, reducing missed or misdiagnosis, and reducing the workload of doctors. In this study, a set of automatic detection methods for cerebral aneurysms based on DSA technology was established. With this method, doctors could obtain three-dimensional (3D) sectional models of suspected aneurysm areas and intracranial arteries for hemodynamic analysis after the input of cranial DSA images. The core of the whole method is the use of 3D FCN, and the detection results can be obtained after the input of images conforming to the standard of Digital Imaging and Communications in Medicine (DICOM).


## 2. Materials and Methods

### 2.1. Research Object

The image data of 130 patients with unruptured intracranial cystic aneurysms who underwent routine physical examination or saw a doctor in our hospital from January 2020 to December 2021 were selected. According to the ratio of the training set and internal test set of about 4 : 1, the patients were randomly divided into three groups: training set (75 cases), internal test set (20 cases), and external test set (35 cases). The age of the patients in the training set and internal test set was 28-86 years old, and 37.5% were aged >60 years. Among them, the average age of the training set patients was56±11 years old, the average age of male patients (24 cases) was 56±10 years old, and the average age of female patients (51 cases) was 58±13 years old. The average age of patients in the internal test set was 56±10 years old, and the average age of male patients (7 cases) was 56±10 years old. The average age of female patients (13 cases) was 56±10 years. Age>60 years old accounted for 42%. Aneurysm sizes in the training set and the internal test set ranged from 1.39 mm to 21.00 mm, and 38.5% of patients had aneurysm sizes<5.00 mm. The training set had a total of 79 aneurysms (4 double cases, 71 single cases), and the internal test set had a total of 26 aneurysms (4 double cases, 1 triple case, and 15 single cases). Aneurysm sizes in the external test set ranged from 2.00 to 23.10 mm, with 40% of patients having aneurysm sizes<5.00 mm, with a total of 35 aneurysms (all single cases). See Table 1. The training set and the internal test set were marked by two radiologists with more than 5 years of experience, and the external test set was marked by two radiologists with more than 3 years of experience, and the DSA results were used as the gold standard.Table 1
Aneurysm condition of the training set and inner and outer test sets.
GroupsNNumber of aneurysms (n)Average aneurysm size (x¯±s, mm)Number of aneurysms (n)<3.00 mm3.00~<5.00 mm5.00~<10.00 mm>10.00 mmTraining set75796.86±4.239233017Internal test set20266.30±3.5636152External test set35356.48±4.00211175


### 2.2. Training Environment and Imaging Methods

The training environment used in this study is as follows: the central processor is Intel Core i9-9900k, the memory is Nvidia DDR4 2400 MHz 32 GB, the graphics card is Nvidia GeForce RTX 2080 Ti, and the operating system is Microsoft Windows 10 professional edition. In this environment, the training process of 200 iterations takes 16 hours, and when detecting aneurysms, the average detection speed is 58 s/case, including the output of blood vessel segmentation results and the output of detection results. Whole cerebral angiography images of all patients with the same parameters were collected: GE Innova 4100 digital flat panel angiography system and three-dimensional workstation. According to the Seldinger method, the femoral artery was punctured, and the internal carotid artery and vertebral artery underwent angiography with a 5F angiography catheter, and the frontal and lateral DSA examinations were performed to obtain DSA images. Due to retrospective data analysis, all cases with unclear images were excluded at enrollment.


### 2.3. Research Methods

First, the dataset is expanded, and the training set is expanded to 600 cases, and the internal test set is expanded to 160 cases by using flipping, discrete Gaussian filtering, and histogram equalization filtering in turn. At the same time, all the data were treated as isotropic and the blank parts around the data were cut out.The automatic aneurysm detection method designed in this study is divided into 2 main steps: (1) automatic segmentation of intracranial arteries and (2) aneurysm detection performed using an FCN-based method.


#### 2.3.1. Cerebral Artery Segmentation

After the DICOM image is preprocessed, nonlinear filtering is used to enhance the grayscale range of blood vessels. The bounding box method is used to automatically select seed points on the surrounding surface of the skull, and through the area growth of the adaptive threshold, the voxels of the skull region can be obtained, and the skull can be automatically removed. Since the voxels in the high-signal value area are mainly blood vessels in the DSA image, binarization is performed on the volume data after removing the skull, and the connected domain statistics are performed on the binarized data [10]. According to the size of the connected domain, pick a seed point. The gray value distribution of blood vessels in the DSA image data is similar to the Gaussian distribution. Using the statistical results of the connected domain, according to the characteristics of the Gaussian distribution, the gray distribution range of the blood vessel region can be automatically determined, and the obtained seed points and the range are used to determine the region. Upon growth, the intracranial arterial vascular tree can be obtained. A 3D reconstruction of the surface of the vessel tree is performed, and the resulting vessel tree model can be used for hemodynamic analysis.


#### 2.3.2. Aneurysm Detection

An optimized 3D U-Net network is used as the detection core [11]. The network is able to handle 128×128×128 voxel data. When training the model, all the data in the training set is first resampled to 128×128×128, and the labeled area is inflated with uniform parameters. There are two kinds of labels in the labeled area, namely, blood vessels and aneurysms. The network training adopts the form of online learning, the learning rate is 5e-2, and the optimization method is adaptive moment estimation (Adam)+stochastic gradient descent (SGD). The training is completed after about 200 iterations and takes about 16 hours.After the training of the network model is completed, the model is used to detect aneurysms. The data of the test set is firstly segmented, and then the segmented vessel tree is detected. The model can give the probability value of aneurysms for each voxel on the vessel tree and adopts the automatic threshold selection method based on the hyperbolic function to classify the detection results into two categories: blood vessels and aneurysms. When displaying the detection result, the center of all voxels of the aneurysm label is taken, and the aneurysm existing in the spherical space with a certain radius (the radius is consistent with the radius of the expansion process) is marked as a prompt to the user. See Figure1.Figure 1
Aneurysm detection procedure.


### 2.4. Observation Indicator

The Dice coefficient was used to determine whether the marked area overlapped with the aneurysm, and the overlapping and nonoverlapping areas were counted. According to whether there is an aneurysm in the marked area as the basis for evaluating the performance of this method, the following two indicators are used: (1) sensitivity: the ratio of the number of labeled areas covered to the total number of aneurysms actually existing and (2) false positive rate: the ratio of the number of labeled areas without aneurysms to the sample size.


### 2.5. Statistical Treatment

Microsoft Excel 2013 software was applied. The normally distributed measurement data are represented byx¯±s, and the count data are represented by frequency (n) and percentage (%).


## 2.1. Research Object

The image data of 130 patients with unruptured intracranial cystic aneurysms who underwent routine physical examination or saw a doctor in our hospital from January 2020 to December 2021 were selected. According to the ratio of the training set and internal test set of about 4 : 1, the patients were randomly divided into three groups: training set (75 cases), internal test set (20 cases), and external test set (35 cases). The age of the patients in the training set and internal test set was 28-86 years old, and 37.5% were aged >60 years. Among them, the average age of the training set patients was56±11 years old, the average age of male patients (24 cases) was 56±10 years old, and the average age of female patients (51 cases) was 58±13 years old. The average age of patients in the internal test set was 56±10 years old, and the average age of male patients (7 cases) was 56±10 years old. The average age of female patients (13 cases) was 56±10 years. Age>60 years old accounted for 42%. Aneurysm sizes in the training set and the internal test set ranged from 1.39 mm to 21.00 mm, and 38.5% of patients had aneurysm sizes<5.00 mm. The training set had a total of 79 aneurysms (4 double cases, 71 single cases), and the internal test set had a total of 26 aneurysms (4 double cases, 1 triple case, and 15 single cases). Aneurysm sizes in the external test set ranged from 2.00 to 23.10 mm, with 40% of patients having aneurysm sizes<5.00 mm, with a total of 35 aneurysms (all single cases). See Table 1. The training set and the internal test set were marked by two radiologists with more than 5 years of experience, and the external test set was marked by two radiologists with more than 3 years of experience, and the DSA results were used as the gold standard.Table 1
Aneurysm condition of the training set and inner and outer test sets.
GroupsNNumber of aneurysms (n)Average aneurysm size (x¯±s, mm)Number of aneurysms (n)<3.00 mm3.00~<5.00 mm5.00~<10.00 mm>10.00 mmTraining set75796.86±4.239233017Internal test set20266.30±3.5636152External test set35356.48±4.00211175


## 2.2. Training Environment and Imaging Methods

The training environment used in this study is as follows: the central processor is Intel Core i9-9900k, the memory is Nvidia DDR4 2400 MHz 32 GB, the graphics card is Nvidia GeForce RTX 2080 Ti, and the operating system is Microsoft Windows 10 professional edition. In this environment, the training process of 200 iterations takes 16 hours, and when detecting aneurysms, the average detection speed is 58 s/case, including the output of blood vessel segmentation results and the output of detection results. Whole cerebral angiography images of all patients with the same parameters were collected: GE Innova 4100 digital flat panel angiography system and three-dimensional workstation. According to the Seldinger method, the femoral artery was punctured, and the internal carotid artery and vertebral artery underwent angiography with a 5F angiography catheter, and the frontal and lateral DSA examinations were performed to obtain DSA images. Due to retrospective data analysis, all cases with unclear images were excluded at enrollment.


## 2.3. Research Methods

First, the dataset is expanded, and the training set is expanded to 600 cases, and the internal test set is expanded to 160 cases by using flipping, discrete Gaussian filtering, and histogram equalization filtering in turn. At the same time, all the data were treated as isotropic and the blank parts around the data were cut out.The automatic aneurysm detection method designed in this study is divided into 2 main steps: (1) automatic segmentation of intracranial arteries and (2) aneurysm detection performed using an FCN-based method.


### 2.3.1. Cerebral Artery Segmentation

After the DICOM image is preprocessed, nonlinear filtering is used to enhance the grayscale range of blood vessels. The bounding box method is used to automatically select seed points on the surrounding surface of the skull, and through the area growth of the adaptive threshold, the voxels of the skull region can be obtained, and the skull can be automatically removed. Since the voxels in the high-signal value area are mainly blood vessels in the DSA image, binarization is performed on the volume data after removing the skull, and the connected domain statistics are performed on the binarized data [10]. According to the size of the connected domain, pick a seed point. The gray value distribution of blood vessels in the DSA image data is similar to the Gaussian distribution. Using the statistical results of the connected domain, according to the characteristics of the Gaussian distribution, the gray distribution range of the blood vessel region can be automatically determined, and the obtained seed points and the range are used to determine the region. Upon growth, the intracranial arterial vascular tree can be obtained. A 3D reconstruction of the surface of the vessel tree is performed, and the resulting vessel tree model can be used for hemodynamic analysis.


### 2.3.2. Aneurysm Detection

An optimized 3D U-Net network is used as the detection core [11]. The network is able to handle 128×128×128 voxel data. When training the model, all the data in the training set is first resampled to 128×128×128, and the labeled area is inflated with uniform parameters. There are two kinds of labels in the labeled area, namely, blood vessels and aneurysms. The network training adopts the form of online learning, the learning rate is 5e-2, and the optimization method is adaptive moment estimation (Adam)+stochastic gradient descent (SGD). The training is completed after about 200 iterations and takes about 16 hours.After the training of the network model is completed, the model is used to detect aneurysms. The data of the test set is firstly segmented, and then the segmented vessel tree is detected. The model can give the probability value of aneurysms for each voxel on the vessel tree and adopts the automatic threshold selection method based on the hyperbolic function to classify the detection results into two categories: blood vessels and aneurysms. When displaying the detection result, the center of all voxels of the aneurysm label is taken, and the aneurysm existing in the spherical space with a certain radius (the radius is consistent with the radius of the expansion process) is marked as a prompt to the user. See Figure1.Figure 1
Aneurysm detection procedure.


## 2.3.1. Cerebral Artery Segmentation

After the DICOM image is preprocessed, nonlinear filtering is used to enhance the grayscale range of blood vessels. The bounding box method is used to automatically select seed points on the surrounding surface of the skull, and through the area growth of the adaptive threshold, the voxels of the skull region can be obtained, and the skull can be automatically removed. Since the voxels in the high-signal value area are mainly blood vessels in the DSA image, binarization is performed on the volume data after removing the skull, and the connected domain statistics are performed on the binarized data [10]. According to the size of the connected domain, pick a seed point. The gray value distribution of blood vessels in the DSA image data is similar to the Gaussian distribution. Using the statistical results of the connected domain, according to the characteristics of the Gaussian distribution, the gray distribution range of the blood vessel region can be automatically determined, and the obtained seed points and the range are used to determine the region. Upon growth, the intracranial arterial vascular tree can be obtained. A 3D reconstruction of the surface of the vessel tree is performed, and the resulting vessel tree model can be used for hemodynamic analysis.


## 2.3.2. Aneurysm Detection

An optimized 3D U-Net network is used as the detection core [11]. The network is able to handle 128×128×128 voxel data. When training the model, all the data in the training set is first resampled to 128×128×128, and the labeled area is inflated with uniform parameters. There are two kinds of labels in the labeled area, namely, blood vessels and aneurysms. The network training adopts the form of online learning, the learning rate is 5e-2, and the optimization method is adaptive moment estimation (Adam)+stochastic gradient descent (SGD). The training is completed after about 200 iterations and takes about 16 hours.After the training of the network model is completed, the model is used to detect aneurysms. The data of the test set is firstly segmented, and then the segmented vessel tree is detected. The model can give the probability value of aneurysms for each voxel on the vessel tree and adopts the automatic threshold selection method based on the hyperbolic function to classify the detection results into two categories: blood vessels and aneurysms. When displaying the detection result, the center of all voxels of the aneurysm label is taken, and the aneurysm existing in the spherical space with a certain radius (the radius is consistent with the radius of the expansion process) is marked as a prompt to the user. See Figure1.Figure 1
Aneurysm detection procedure.


## 2.4. Observation Indicator

The Dice coefficient was used to determine whether the marked area overlapped with the aneurysm, and the overlapping and nonoverlapping areas were counted. According to whether there is an aneurysm in the marked area as the basis for evaluating the performance of this method, the following two indicators are used: (1) sensitivity: the ratio of the number of labeled areas covered to the total number of aneurysms actually existing and (2) false positive rate: the ratio of the number of labeled areas without aneurysms to the sample size.


## 2.5. Statistical Treatment

Microsoft Excel 2013 software was applied. The normally distributed measurement data are represented byx¯±s, and the count data are represented by frequency (n) and percentage (%).


## 3. Results

In this study, the dataset is randomly divided into three subsets: training set, internal test set, and external test set. Among them, the external test set does not participate in the training and parameter adjustment process at all.This study used fivefold cross-validation on the training set and internal test set with an average sensitivity of94.4±1.1%. For the external test set, the detection sensitivity of this method is 82.9%, and the false positive rate is 0.86 FPs/case (false positives/case). A classification comparison of the external test set data showed that the sensitivity (88.2% and 100.0%) of the method for detecting aneurysms with a size of 5.00~<10.00 mm and ≥10.00 mm was higher than that for aneurysms with a size of <3.00 mm and 3.00~<5.00 mm (50.0% and 72.7%). The detection sensitivity of patients aged 50-60 years and >60 years (90.0% and 87.5%) was higher than that of patients aged <50 years (66.7%). The difference in detection sensitivity between different genders (84.6% and 81.8% for males and females, respectively) was small. See Table 2.Table 2
External test set comparison of aneurysm detection methods (N=35).
Clinical featuresNAneurysm detection methodsDetection sensitivity (%)Manual marking+DSA (n)Automatic detection (n)GenderMale13131184.6Female22221881.8Age<50 years old99666.750~60 years old1010990.0>60 years old16161487.5Aneurysm size<3.00 mm22150.03.00~<5.00 mm1111872.75.00 mm~<10.00 mm17171588.2≥10.00 mm555100.0Among the 6 undetected aneurysms (false negatives) in the external test set, there were 4 females and 2 males, aged 35-69 years, with aneurysm sizes ranging from 2.60 to 5.67 mm. There were 30 false positive results: 17 cases were found, 5 cases were 2 cases, and 1 case was 3 cases; the remaining 12 cases had no false positive results.


## 4. Discussion

Intracranial aneurysms are cerebral hemangioma-like protrusions caused by abnormal changes in local blood vessel morphology. The etiology of aneurysms is still unclear. Congenital factors include the following: the wall thickness of the cerebral artery is about 1/3 thinner than that of other parts of the same diameter, and it lacks the support of surrounding tissues, so it bears a large blood flow, especially at the bifurcation. Acquired factors include infection, trauma, tumor, and atherosclerosis; most of them are congenital factors. The formation mechanism of intracranial aneurysms is the stress damage of blood flow to the arterial wall. Due to the change in blood flow, a part of the arterial wall protrudes outward, forming a permanent local expansion. The magnitude of the stress is usually related to the velocity and angle of the blood vessel. Intracranial aneurysm is classified as a serious cerebrovascular disease due to its insidious onset, complex pathogenesis, and critical onset [12].DSA combines rotational angiography, DSA technology, and computer three-dimensional image processing technology. After secondary rotation DSA collects image data, it can observe and accurately measure lesions at any angle, thereby reducing the number of angiography images. From the aspect of morphology, clear observation is needed. The relationship between the tumor body and the parent artery and the diameter of the tumor diameter can be analyzed, and the adjacent relationship with the surrounding blood vessels and the blood circulation can be analyzed, the quality of image acquisition and the safety of interventional diagnosis and treatment can be improved, and the corresponding treatment can be performed after angiography [13].All data in this study were DSA images collected with uniform parameters from outpatients and routine physical examinations. The age, gender, aneurysm location, and aneurysm size of the subjects were randomly distributed. By analyzing the results on the external test set, it can be seen that in the detection method of this study, different genders and different ages have little effect on the processing performance, but the aneurysm size has a greater impact on the processing performance. In this study, the sensitivity of the method for detecting aneurysm samples withsize≥10.00 mm was 100.0% (including the internal test set and external test set), and the sensitivity for detecting aneurysms with size 5.00~<10.00 mm was better than that for aneurysms with size 3.00~<5.00 mm. Therefore, considering the detection performance of aneurysms of different sizes, the detection ability of aneurysms with a size of ≥5 mm is better than that of aneurysms with a size of <5.0 mm. The results could not demonstrate the detection performance of this method for aneurysms of this size. In terms of false positives, the average false positive rate in this study was 0.86 FPs/case, and the number of false positives in each case was 0-3.The results of this study show that the detection method is not sensitive to the age of the samples. The ability of these methods to detect aneurysms inpatients≥50 years old is better than that in patients<50 years old. Since the data used in this study all use a unified acquisition matrix, the sharpness of the images does not change in different samples, so the detection performance of the detection method in this study on other sharpness samples cannot be proved. However, in terms of the distribution of the false negative results of this method in the samples, the FCN used in the study is suitable for the task of aneurysm detection, and increasing the data volume of the training samples is expected to improve the performance of the method. In terms of false positive performance, the false positive results obtained in this study are mainly aneurysms with a size of <3.00 mm. Due to the process, the false positive results are all located on the cerebral arteries, which need to be confirmed by the user twice, but the average false positive rate is higher. In addition, the number of false positives in a single sample is at most 3, and the workload of secondary confirmation is relatively small. Under the current false positive rate conditions, it is considered to give priority to improving the sensitivity of the method to avoid missed diagnosis.This study is based on the detection method of deep learning, with a sample size of 130 cases, and the obtained result has a sensitivity of 82.9% and a false positive rate of 0.86 FPs/case. Ueda et al. [14] used a two-dimensional ResNet-18 network, Nakao et al. [15] used a 4-layer CNN and sliced the data in multiple directions, and Hanaoka et al. [16] used a method based on artificial features and support vector machines to detect aneurysms. The comparison shows that the sensitivity and false positive rate of this study are better than the results of Hanaoka et al. (sensitivity 80.0%, false positive rate 3.00 FPs/case). Its effect is better than the traditional artificial feature method. However, the sample size in the study by Nakao et al. is smaller than that in the study by Ueda et al., and the network with fewer layers achieves better results, which may indicate that in the case of sufficient sample size, the network structure has a significant impact on the detection results. Shallow networks have the potential to outperform deep networks in the task of aneurysm detection. The sample size of this study is significantly smaller than that of Nakao et al. (450 cases) and Ueda et al. (1271 cases), but the number of samples required by the 3D FCN used is larger than that of the two-dimensional network (the two-dimensional network can convert each slice that is regarded as 1 sample, while the 3D network treats each aneurysm as 1 sample), so the sensitivity is lower than that of Nakao et al. and Ueda et al. The study by Ueda et al. (2.90 and 6.60 FPs/case) also illustrates the potential of 3D FCN in aneurysm detection.To sum up, the detection method in this study has similar detection performance for different types of data (age, gender, and aneurysm size), and it also proves that 3D FCN has a good effect on structures such as bends, bifurcations, and aneurysms on blood vessels (recognition ability). However, 3D networks have higher requirements on the amount of training data, and the noise in the data has a greater impact on the training results. At present, the sensitivity of the detection method in this study is 82.9%, which is still a certain distance from clinical application. In future research, we will focus on improving methods, such as data expansion and network model layer reduction, in order to improve the detection method in this study and overall performance.



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*Source: 8485651-2022-08-13.xml*