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Song G, Li K, Wang Z, Liu W, Xue Q, Liang J, Zhou Y, Geng H, Liu D. A fully automatic radiomics pipeline for postoperative facial nerve function prediction of vestibular schwannoma. Neuroscience 2025; 574:124-137. [PMID: 40210197 DOI: 10.1016/j.neuroscience.2025.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2025] [Revised: 03/28/2025] [Accepted: 04/05/2025] [Indexed: 04/12/2025]
Abstract
Vestibular schwannoma (VS) is the most prevalent intracranial schwannoma. Surgery is one of the options for the treatment of VS, with the preservation of facial nerve (FN) function being the primary objective. Therefore, postoperative FN function prediction is essential. However, achieving automation for such a method remains a challenge. In this study, we proposed a fully automatic deep learning approach based on multi-sequence magnetic resonance imaging (MRI) to predict FN function after surgery in VS patients. We first developed a segmentation network 2.5D Trans-UNet, which combined Transformer and U-Net to optimize contour segmentation for radiomic feature extraction. Next, we built a deep learning network based on the integration of 1DConvolutional Neural Network (1DCNN) and Gated Recurrent Unit (GRU) to predict postoperative FN function using the extracted features. We trained and tested the 2.5D Trans-UNet segmentation network on public and private datasets, achieving accuracies of 89.51% and 90.66%, respectively, confirming the model's strong performance. Then Feature extraction and selection were performed on the private dataset's segmentation results using 2.5D Trans-UNet. The selected features were used to train the 1DCNN-GRU network for classification. The results showed that our proposed fully automatic radiomics pipeline outperformed the traditional radiomics pipeline on the test set, achieving an accuracy of 88.64%, demonstrating its effectiveness in predicting the postoperative FN function in VS patients. Our proposed automatic method has the potential to become a valuable decision-making tool in neurosurgery, assisting neurosurgeons in making more informed decisions regarding surgical interventions and improving the treatment of VS patients.
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Affiliation(s)
- Gang Song
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Keyuan Li
- School of Information Science and Technology, Beijing University of Technology, Beijing, China
| | - Zhuozheng Wang
- School of Information Science and Technology, Beijing University of Technology, Beijing, China
| | - Wei Liu
- School of Information Science and Technology, Beijing University of Technology, Beijing, China.
| | - Qi Xue
- School of Information Science and Technology, Beijing University of Technology, Beijing, China
| | - Jiantao Liang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yiqiang Zhou
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Haoming Geng
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Dong Liu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
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Łajczak P, Matyja J, Jóźwik K, Nawrat Z. Accuracy of vestibular schwannoma segmentation using deep learning models - a systematic review & meta-analysis. Neuroradiology 2025; 67:729-742. [PMID: 39179652 PMCID: PMC12003617 DOI: 10.1007/s00234-024-03449-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 08/09/2024] [Indexed: 08/26/2024]
Abstract
Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60-69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. However, a knowledge gap exists in the automated segmentation of VS using DL. To address this gap, this meta-analysis aims to provide insights into the current state of DL algorithms applied to MR images of VS. METHODOLOGY Following 2020 PRISMA guidelines, a search across four databases was conducted. Inclusion criteria focused on articles using DL for VS MR image segmentation. The primary metric was the Dice score, supplemented by relative volume error (RVE) and average symmetric surface distance (ASSD). RESULTS The search process identified 752 articles, leading to 11 studies for meta-analysis. A QUADAS- 2 analysis revealed varying biases. The overall Dice score for 56 models was 0.89 (CI: 0.88-0.90), with high heterogeneity (I2 = 95.9%). Subgroup analyses based on DL architecture, MRI inputs, and testing set sizes revealed performance variations. 2.5D DL networks demonstrated comparable efficacy to 3D networks. Imaging input analyses highlighted the superiority of contrast-enhanced T1-weighted imaging and mixed MRI inputs. DISCUSSION This study fills a gap in systematic review in the automated segmentation of VS using DL techniques. Despite promising results, limitations include publication bias and high heterogeneity. Future research should focus on standardized designs, larger testing sets, and addressing biases for more reliable results. DL have promising efficacy in VS diagnosis, however further validation and standardization is needed. CONCLUSION In conclusion, this meta-analysis provides comprehensive review into the current landscape of automated VS segmentation using DL. The high Dice score indicates promising agreement in segmentation, yet challenges like bias and heterogeneity must be addressed in the future research.
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Affiliation(s)
- Paweł Łajczak
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Jordana 18, Mekelweg 5, Zabrze, 40-043,, Poland.
| | - Jakub Matyja
- TU Delft, Mekelweg 5,, Delft 2628 CD,, Netherlands
| | - Kamil Jóźwik
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Jordana 18, Mekelweg 5, Zabrze, 40-043,, Poland
| | - Zbigniew Nawrat
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Jordana 18, Mekelweg 5, Zabrze, 40-043,, Poland
- Foundation of Cardiac Surgery Development, Zabrze, 41-808, Poland
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Wu J, Guo D, Wang G, Yue Q, Yu H, Li K, Zhang S. FPL+: Filtered Pseudo Label-Based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:3098-3109. [PMID: 38602852 DOI: 10.1109/tmi.2024.3387415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are needed for the adaptation. Existing UDA methods are mainly based on image or feature alignment with adversarial training for regularization, and they are limited by insufficient supervision in the target domain. In this paper, we propose an enhanced Filtered Pseudo Label (FPL+)-based UDA method for 3D medical image segmentation. It first uses cross-domain data augmentation to translate labeled images in the source domain to a dual-domain training set consisting of a pseudo source-domain set and a pseudo target-domain set. To leverage the dual-domain augmented images to train a pseudo label generator, domain-specific batch normalization layers are used to deal with the domain shift while learning the domain-invariant structure features, generating high-quality pseudo labels for target-domain images. We then combine labeled source-domain images and target-domain images with pseudo labels to train a final segmentor, where image-level weighting based on uncertainty estimation and pixel-level weighting based on dual-domain consensus are proposed to mitigate the adverse effect of noisy pseudo labels. Experiments on three public multi-modal datasets for Vestibular Schwannoma, brain tumor and whole heart segmentation show that our method surpassed ten state-of-the-art UDA methods, and it even achieved better results than fully supervised learning in the target domain in some cases.
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Bonada M, Rossi LF, Carone G, Panico F, Cofano F, Fiaschi P, Garbossa D, Di Meco F, Bianconi A. Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field. Biomedicines 2024; 12:1878. [PMID: 39200342 PMCID: PMC11352020 DOI: 10.3390/biomedicines12081878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 07/29/2024] [Accepted: 07/31/2024] [Indexed: 09/02/2024] Open
Abstract
Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging (MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and prognostic information. We comprehensively overviewed the currently available DL applications, critically examining the limitations that hinder their broader adoption in clinical practice and molecular research. Technical limitations to the routine application of DL include the qualitative heterogeneity of MRI, related to different machinery and protocols, and the absence of informative sequences, possibly compensated by artificial image synthesis. Moreover, taking advantage from the available benchmarks of MRI, algorithms should be trained on large amounts of data. Additionally, the segmentation of postoperative imaging should be further addressed to limit the inaccuracies previously observed for this task. Indeed, molecular information has been promisingly integrated in the most recent DL tools, providing useful prognostic and therapeutic information. Finally, ethical concerns should be carefully addressed and standardized to allow for data protection. DL has provided reliable results for GBM assessment concerning MRI analysis and segmentation, but the routine clinical application is still limited. The current limitations could be prospectively addressed, giving particular attention to data collection, introducing new technical advancements, and carefully regulating ethical issues.
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Affiliation(s)
- Marta Bonada
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy; (G.C.)
| | - Luca Francesco Rossi
- Department of Informatics, Polytechnic University of Turin, Corso Castelfidardo 39, 10129 Turin, Italy;
| | - Giovanni Carone
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy; (G.C.)
| | - Flavio Panico
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
| | - Fabio Cofano
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
| | - Pietro Fiaschi
- Division of Neurosurgery, Ospedale Policlinico San Martino, IRCCS for Oncology and Neurosciences, Largo Rosanna Benzi 10, 16132 Genoa, Italy;
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Largo Rosanna Benzi 10, 16132 Genoa, Italy
| | - Diego Garbossa
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
| | - Francesco Di Meco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy; (G.C.)
| | - Andrea Bianconi
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
- Division of Neurosurgery, Ospedale Policlinico San Martino, IRCCS for Oncology and Neurosciences, Largo Rosanna Benzi 10, 16132 Genoa, Italy;
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Nernekli K, Persad AR, Hori YS, Yener U, Celtikci E, Sahin MC, Sozer A, Sozer B, Park DJ, Chang SD. Automatic Segmentation of Vestibular Schwannomas: A Systematic Review. World Neurosurg 2024; 188:35-44. [PMID: 38685346 DOI: 10.1016/j.wneu.2024.04.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND Vestibular schwannomas (VSs) are benign tumors often monitored over time, with measurement techniques for assessing growth rates subject to significant interobserver variability. Automatic segmentation of these tumors could provide a more reliable and efficient for tracking their progression, especially given the irregular shape and growth patterns of VS. METHODS Various studies and segmentation techniques employing different Convolutional Neural Network architectures and models, such as U-Net and convolutional-attention transformer segmentation, were analyzed. Models were evaluated based on their performance across diverse datasets, and challenges, including domain shift and data sharing, were scrutinized. RESULTS Automatic segmentation methods offer a promising alternative to conventional measurement techniques, offering potential benefits in precision and efficiency. However, these methods are not without challenges, notably the "domain shift" that occurs when models trained on specific datasets underperform when applied to different datasets. Techniques such as domain adaptation, domain generalization, and data diversity were discussed as potential solutions. CONCLUSIONS Accurate measurement of VS growth is a complex process, with volumetric analysis currently appearing more reliable than linear measurements. Automatic segmentation, despite its challenges, offers a promising avenue for future investigation. Robust well-generalized models could potentially improve the efficiency of tracking tumor growth, thereby augmenting clinical decision-making. Further work needs to be done to develop more robust models, address the domain shift, and enable secure data sharing for wider applicability.
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Affiliation(s)
- Kerem Nernekli
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
| | - Amit R Persad
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Yusuke S Hori
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Ulas Yener
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Emrah Celtikci
- Department of Neurosurgery, Gazi University, Ankara, Turkey
| | | | - Alperen Sozer
- Department of Neurosurgery, Gazi University, Ankara, Turkey
| | - Batuhan Sozer
- Department of Neurosurgery, Gazi University, Ankara, Turkey
| | - David J Park
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA.
| | - Steven D Chang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
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Byeon H, Al-Kubaisi M, Dutta AK, Alghayadh F, Soni M, Bhende M, Chunduri V, Suresh Babu K, Jeet R. Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model. Front Comput Neurosci 2024; 18:1391025. [PMID: 38634017 PMCID: PMC11021780 DOI: 10.3389/fncom.2024.1391025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper's objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network's ability to gather long-distance dependencies for AI, Expectation-Maximization is applied to the cascade network's lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network's ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network's standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.
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Affiliation(s)
- Haewon Byeon
- Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, Republic of Korea
| | - Mohannad Al-Kubaisi
- Department of Computer Science, Al-Maarif University College, Al-Anbar Governorate, Iraq
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Faisal Alghayadh
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Department of CSE, University Centre for Research and Development, Chandigarh University, Mohali, Punjab, India
| | - Manisha Bhende
- Dr. D. Y. Patil Vidyapeeth, Pune, Dr. D. Y. Patil School of Science & Technology, Tathawade, Pune, India
| | - Venkata Chunduri
- Department of Mathematics and Computer Science, Indiana State University, Terre Haute, IN, United States
| | - K. Suresh Babu
- Department of Biochemistry, Symbiosis Medical College for Women, Symbiosis International (Deemed University), Pune, India
| | - Rubal Jeet
- Chandigarh Engineering College, Jhanjeri, Mohali, India
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Alsaleh H. The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review. Technol Health Care 2024; 32:3801-3813. [PMID: 39093085 PMCID: PMC11612958 DOI: 10.3233/thc-232043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 05/20/2024] [Indexed: 08/04/2024]
Abstract
BACKGROUND Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors and preferences. OBJECTIVE The purpose of this study is to investigate how machine learning and artificial intelligence (AI) can revolutionise AN management and diagnostic procedures. METHODS A thorough systematic review that included peer-reviewed material from public databases was carried out. Publications on AN, AI, and deep learning up until December 2023 were included in the review's purview. RESULTS Based on our analysis, AI models for volume estimation, segmentation, tumour type differentiation, and separation from healthy tissues have been developed successfully. Developments in computational biology imply that AI can be used effectively in a variety of fields, including quality of life evaluations, monitoring, robotic-assisted surgery, feature extraction, radiomics, image analysis, clinical decision support systems, and treatment planning. CONCLUSION For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data. Subsequent investigations ought to concentrate on reproducing findings in order to standardise AI approaches, which could transform their use in medical environments.
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Affiliation(s)
- Hadeel Alsaleh
- Department of Communication Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
- E-mail:
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Wu J, Wang G, Gu R, Lu T, Chen Y, Zhu W, Vercauteren T, Ourselin S, Zhang S. UPL-SFDA: Uncertainty-Aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3932-3943. [PMID: 37738202 DOI: 10.1109/tmi.2023.3318364] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are usually unavailable when a trained model is deployed at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and annotation-efficient adaptation to the target domain. However, existing SFDA methods have a limited performance due to lack of sufficient supervision with source-domain images unavailable and target-domain images unlabeled. We propose a novel Uncertainty-aware Pseudo Label guided (UPL) SFDA method for medical image segmentation. Specifically, we propose Target Domain Growing (TDG) to enhance the diversity of predictions in the target domain by duplicating the pre-trained model's prediction head multiple times with perturbations. The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels. We also propose a Twice Forward pass Supervision (TFS) strategy that uses reliable pseudo labels obtained in one forward pass to supervise predictions in the next forward pass. The adaptation is further regularized by a mean prediction-based entropy minimization term that encourages confident and consistent results in different prediction heads. UPL-SFDA was validated with a multi-site heart MRI segmentation dataset, a cross-modality fetal brain segmentation dataset, and a 3D fetal tissue segmentation dataset. It improved the average Dice by 5.54, 5.01 and 6.89 percentage points for the three tasks compared with the baseline, respectively, and outperformed several state-of-the-art SFDA methods.
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Wu Z, Zhang X, Li F, Wang S, Li J. TransRender: a transformer-based boundary rendering segmentation network for stroke lesions. Front Neurosci 2023; 17:1259677. [PMID: 37901438 PMCID: PMC10601640 DOI: 10.3389/fnins.2023.1259677] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023] Open
Abstract
Vision transformer architectures attract widespread interest due to their robust representation capabilities of global features. Transformer-based methods as the encoder achieve superior performance compared to convolutional neural networks and other popular networks in many segmentation tasks for medical images. Due to the complex structure of the brain and the approximate grayscale of healthy tissue and lesions, lesion segmentation suffers from over-smooth boundaries or inaccurate segmentation. Existing methods, including the transformer, utilize stacked convolutional layers as the decoder to uniformly treat each pixel as a grid, which is convenient for feature computation. However, they often neglect the high-frequency features of the boundary and focus excessively on the region features. We propose an effective method for lesion boundary rendering called TransRender, which adaptively selects a series of important points to compute the boundary features in a point-based rendering way. The transformer-based method is selected to capture global information during the encoding stage. Several renders efficiently map the encoded features of different levels to the original spatial resolution by combining global and local features. Furthermore, the point-based function is employed to supervise the render module generating points, so that TransRender can continuously refine the uncertainty region. We conducted substantial experiments on different stroke lesion segmentation datasets to prove the efficiency of TransRender. Several evaluation metrics illustrate that our method can automatically segment the stroke lesion with relatively high accuracy and low calculation complexity.
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Affiliation(s)
- Zelin Wu
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Xueying Zhang
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Fenglian Li
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Suzhe Wang
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Jiaying Li
- The First Clinical Medical College, Shanxi Medical University, Taiyuan, China
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