3D PBV-Net: An automated prostate MRI data segmentation method

Jin, Yao Hong; Yang, Guang; Fang, Ying; Li, Ruipeng; Xu, Xiaomei; Liu, Yongkai; Lai, Xiaobo

doi:10.1016/j.compbiomed.2020.104160

Cited by 78 publications

(24 citation statements)

References 13 publications

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“…In recent years, MRI has a high accuracy in diagnosing the severity of prenatal PA and can observe other combined lesions, such as fetal brain development and intracranial hemorrhage. Therefore, MRI can be used as an important inspection method for PA during pregnancy [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

Clinical Analysis of Improved Particle Swarm Algorithm-Based Magnetic Resonance Imaging Diagnosis of Placenta Accreta

Ding

Cao

Sun

et al. 2021

Contrast Media & Molecular Imaging

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The magnetic resonance imaging (MRI) image processing capabilities were investigated based on the improved particle swarm optimization (IPSO) algorithm, and the clinical application analysis of MRI images in the diagnosis of placenta accreta (PA) was evaluated in this study. The MRI uterine images were detected on the basis of IPSO. Besides, the clinical data of 89 patients with PA were selected and collected, who were diagnosed by clinical cesarean section surgery and pathological comprehensive diagnosis in hospital from January 2018 to July 2020. Then, all of them underwent the ultrasound (US) and MRI examinations, and the differences of sensitivity, specificity, and accuracy between MRI and US under IPSO in the diagnosis of PA were compared, as well as the differences in the diagnosis of adhesive, implantable, and penetrated PA. The results showed that the difference in detection between IPSO-based MRI images and US images was not statistically substantial ( p > 0.05 ), but the number of initial detections was higher than the number of US examination. MRI examination had higher sensitivity and specificity in the diagnosis of PA during pregnancy, especially for implantable PA, compared with US examination ( p < 0.05 ). In conclusion, MRI images based on the improved particle swarm optimization algorithm showed a good application effect in the diagnosis of placental implantation diseases, which was worthy of further promotion in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Clinical Analysis of Improved Particle Swarm Algorithm-Based Magnetic Resonance Imaging Diagnosis of Placenta Accreta

Ding

Cao

Sun

et al. 2021

Contrast Media & Molecular Imaging

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“…Yaniv et al, segmented the prostate based on 3D V-net [45] and optimized the speed and memory limitations of this network [46]. Additionally, Jin et al, applied bicubic interpolation to preprocess the low frequency part of the prostate MR image, and then used an improved 3D V-Net (3D PBV-Net) to segment the prostate [47]. Silva et al, proposed a two-stage prostate segmentation method.…”

Section: Related Workmentioning

confidence: 99%

ProSegNet: A New Network of Prostate Segmentation Based on MR Images

Ye¹

2021

IEEE Access

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Prostate cancer is the most common cancer in men after lung cancer. Generally, the segmentation of the prostate is the preprocessing work for the diagnosis of prostate cancer. Aiming at the variety of prostate and the similarity of visual characteristics between prostates and their surroundings, this paper proposes a new prostate segmentation network based on MR images, denoted as ProSegNet. ProSegNet consists of two parts: encoder and decoder. To improve the feature extraction capability of the encoder, we use dense blocks as the feature extraction unit, and at the same time introduce a cross-stage partial (CSP) structure to reduce the amount of calculation. In the design of the decoder structure, we integrated the spatial attention mechanism and the channel attention mechanism to enable it to focus on the important features while ignoring the invalid features. In addition, to segment the prostate more accurately, we add a prostate contour segmentation branch to the output of the segmentation network to learn the contour features of the prostate. Finally, to alleviate the problem of small intensity difference between the prostate and surrounding tissues, we designed a truncated intensity stretching image enhancement method. The performance of ProSegNet has been experimentally verified in the Promise12 and ProstateX datasets. On the Promise12 dataset, the dice similarity coefficient (DSC) and hausdorff (Haus) distance are 0.908 and 9.87 respectively. On the ProstateX dataset, the DSC and Haus reach the results of 0.892 and 10.45, respectively. Experimental results show that the ProSegNet can obtain a competitive performance.INDEX TERMS prostate segmentation, magnetic resonance image, convolutional neural network, attention mechanism, multi-task learning.

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“…In recent years, deep convolutional neural networks (CNNs) ( 24 ) with complex network structures have achieved remarkable results in the field of computer vision, such as tumor grade prediction, patient prognosis, pathology classification, and organ segmentation ( 25 , 26 ). The successful application of deep learning requires a large number of training cohort sets.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features

Zhang

Jiang

et al. 2021

Front. Med.

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Objectives: To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI).Methods: This single-center retrospective analysis involved 188 patients with pathologically proven brain abscess (102) or cystic glioma (86). One thousand DTL and 105 HCR features were extracted from the T1WI and T2WI of the patients. Three feature selection methods and four classifiers, such as k-nearest neighbors (KNN), random forest classifier (RFC), logistic regression (LR), and support vector machine (SVM), for distinguishing brain abscess from cystic glioma were compared. The best feature combination and classifier were chosen according to the quantitative metrics including area under the curve (AUC), Youden Index, and accuracy.Results: In most cases, deep learning-based radiomics (DLR) features, i.e., DTL features combined with HCR features, contributed to a higher accuracy than HCR and DTL features alone for distinguishing brain abscesses from cystic gliomas. The AUC values of the model established, based on the DLR features in T2WI, were 0.86 (95% CI: 0.81, 0.91) in the training cohort and 0.85 (95% CI: 0.75, 0.95) in the test cohort, respectively.Conclusions: The model established with the DLR features can distinguish brain abscess from cystic glioma efficiently, providing a useful, inexpensive, convenient, and non-invasive method for differential diagnosis. This is the first time that conventional MRI radiomics is applied to identify these diseases. Also, the combination of HCR and DTL features can lead to get impressive performance.

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3D PBV-Net: An automated prostate MRI data segmentation method

Cited by 78 publications

References 13 publications

Clinical Analysis of Improved Particle Swarm Algorithm-Based Magnetic Resonance Imaging Diagnosis of Placenta Accreta

Clinical Analysis of Improved Particle Swarm Algorithm-Based Magnetic Resonance Imaging Diagnosis of Placenta Accreta

ProSegNet: A New Network of Prostate Segmentation Based on MR Images

Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features

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