Brain SegNet: 3D Local Refinement Network for Brain Lesion Segmentation

Hu, Xiaojun; Luo, Weijian; Hu, Jiliang; Guo, Sheng; Huang, Weilin; Scott, Matthew R.; Wiest, Roland; Dahlweid, Michael

doi:10.21203/rs.2.14749/v3

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…e segmentation results of the MSCC-MDF established in this research were compared with those of CNN [21], fully CNN (FCN) [22], SegNet [23], and deep Q-network (DQN) [24] (Figure 9). e Dice coefficient of the MSCC-MDF model was lower than other algorithms, and the difference was considerable (P < 0.05).…”

Section: Tumor Lesion Segmentation Results By Mscc-mdfmentioning

confidence: 99%

Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci

Wang

Gong

Zhang

et al. 2021

Contrast Media & Molecular Imaging

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The aim of this study was to explore the adoption value of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image intelligent segmentation model in the identification of nasopharyngeal carcinoma (NPC) lesions. The multisequence cross convolutional (MSCC) method was used in the complex convolutional network algorithm to establish the intelligent segmentation model two-dimensional (2D) ResUNet for the MRI image of the NPC lesion. Moreover, a multisequence multidimensional fusion segmentation model (MSCC-MDF) was further established. With 45 patients with NPC as the research objects, the Dice coefficient, Hausdorff distance (HD), and percentage of area difference (PAD) were calculated to evaluate the segmentation effect of MRI lesions. The results showed that the 2D-ResUNet model processed by MSCC had the largest Dice coefficient of 0.792 ± 0.045 for segmenting the tumor lesions of NPC, and it also had the smallest HD and PAD, which were 5.94 ± 0.41 mm and 15.96 ± 1.232%, respectively. When batch size = 5, the convergence curve was relatively gentle, and the convergence speed was the best. The largest Dice coefficient of MSCC-MDF model segmenting NPC tumor lesions was 0.896 ± 0.09, and its HD and PAD were the smallest, which were 5.07 ± 0.54 mm and 14.41 ± 1.33%, respectively. Its Dice coefficient was lower than other algorithms ( P < 0.05 ), but HD and PAD were significantly higher than other algorithms ( P < 0.05 ). To sum up, the MSCC-MDF model significantly improved the segmentation performance of MRI lesions in NPC patients, which provided a reference for the diagnosis of NPC.

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Section: Tumor Lesion Segmentation Results By Mscc-mdfmentioning

confidence: 99%

Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci

Wang

Gong

Zhang

et al. 2021

Contrast Media & Molecular Imaging

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“…Seg is a brand-new deep fully convolutional neural network model (semantic pixel-wise neural network model) proposed by three deep learning experts (Yadri-narayanan, Kcndall, and Cipolla) from the University of Cambridge in 2010 that can perform image pixel-wise semantic division and image labeling [ 15 ]. The SegNet model is mainly composed of encoder, decoder, and oft-max layer.…”

Section: Segnet Modelmentioning

confidence: 99%

Ultrasound Images Guided under Deep Learning in the Anesthesia Effect of the Regional Nerve Block on Scapular Fracture Surgery

Liu

Cheng

2021

Journal of Healthcare Engineering

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In order to discuss the clinical characteristics of patients with scapular fracture, deep learning model was adopted in ultrasound images of patients to locate the anesthesia point of patients during scapular fracture surgery treated with the regional nerve block. 100 patients with scapular fracture who were hospitalized for emergency treatment in the hospital were recruited. Patients in the algorithm group used ultrasound-guided regional nerve block puncture, and patients in the control group used traditional body surface anatomy for anesthesia positioning. The ultrasound images of the scapula of the contrast group were used for the identification of the deep learning model and analysis of anesthesia acupuncture sites. The ultrasound images of the scapula anatomy of the patients in the contrast group were extracted, and the convolutional neural network model was employed for training and test. Moreover, the model performance was evaluated. It was found that the adoption of deep learning greatly improved the accuracy of the image. It took an average of 7.5 ± 2.07 minutes from the time the puncture needle touched the skin to the completion of the injection in the algorithm group (treated with artificial intelligence ultrasound positioning). The operation time of the control group (anatomical positioning) averaged 10.2 ± 2.62 min. Moreover, there was a significant difference between the two groups ( p < 0.05 ). The method adopted in the contrast group had high positioning accuracy and good anesthesia effect, and the patients had reduced postoperative complications of patients (all P < 0.005 ). The deep learning model can effectively improve the accuracy of ultrasound images and measure and assist the treatment of future clinical cases of scapular fractures. While improving medical efficiency, it can also accurately identify patient fractures, which has great adoption potential in improving the effect of surgical anesthesia.

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“…Most are based on DL with various architectures and attention gates are commonly adopted to improve performance by automatically highlighting informative elements of intermediate feature maps 73 . Hu et al proposed a novel 3D refinement module that can aggregate local detail information and 3D semantic context directly within the 3D convolutional layer 74 . Kamnitsas et al developed a 3D‐CNN with a dual pathway and 11 convolutional layers 59 .…”

Section: Ai In Tumor Subregion Analysis Of Medical Imagesmentioning

confidence: 99%

Artificial intelligence in tumor subregion analysis based on medical imaging: A review

Lin

Wynne

Zhou

et al. 2021

J Applied Clin Med Phys

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Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential applications of AI in tumor subregion analysis are discussed.

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Brain SegNet: 3D Local Refinement Network for Brain Lesion Segmentation

Cited by 7 publications

References 18 publications

Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci

Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci

Ultrasound Images Guided under Deep Learning in the Anesthesia Effect of the Regional Nerve Block on Scapular Fracture Surgery

Artificial intelligence in tumor subregion analysis based on medical imaging: A review

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