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Gutsche R, Lowis C, Ziemons K, Kocher M, Ceccon G, Régio Brambilla C, Shah NJ, Langen KJ, Galldiks N, Isensee F, Lohmann P. Automated Brain Tumor Detection and Segmentation for Treatment Response Assessment Using Amino Acid PET. J Nucl Med 2023; 64:1594-1602. [PMID: 37562802 DOI: 10.2967/jnumed.123.265725] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/31/2023] [Indexed: 08/12/2023] Open
Abstract
Evaluation of metabolic tumor volume (MTV) changes using amino acid PET has become an important tool for response assessment in brain tumor patients. MTV is usually determined by manual or semiautomatic delineation, which is laborious and may be prone to intra- and interobserver variability. The goal of our study was to develop a method for automated MTV segmentation and to evaluate its performance for response assessment in patients with gliomas. Methods: In total, 699 amino acid PET scans using the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) from 555 brain tumor patients at initial diagnosis or during follow-up were retrospectively evaluated (mainly glioma patients, 76%). 18F-FET PET MTVs were segmented semiautomatically by experienced readers. An artificial neural network (no new U-Net) was configured on 476 scans from 399 patients, and the network performance was evaluated on a test dataset including 223 scans from 156 patients. Surface and volumetric Dice similarity coefficients (DSCs) were used to evaluate segmentation quality. Finally, the network was applied to a recently published 18F-FET PET study on response assessment in glioblastoma patients treated with adjuvant temozolomide chemotherapy for a fully automated response assessment in comparison to an experienced physician. Results: In the test dataset, 92% of lesions with increased uptake (n = 189) and 85% of lesions with iso- or hypometabolic uptake (n = 33) were correctly identified (F1 score, 92%). Single lesions with a contiguous uptake had the highest DSC, followed by lesions with heterogeneous, noncontiguous uptake and multifocal lesions (surface DSC: 0.96, 0.93, and 0.81 respectively; volume DSC: 0.83, 0.77, and 0.67, respectively). Change in MTV, as detected by the automated segmentation, was a significant determinant of disease-free and overall survival, in agreement with the physician's assessment. Conclusion: Our deep learning-based 18F-FET PET segmentation allows reliable, robust, and fully automated evaluation of MTV in brain tumor patients and demonstrates clinical value for automated response assessment.
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Affiliation(s)
- Robin Gutsche
- Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Carsten Lowis
- Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - Karl Ziemons
- Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, Juelich, Germany
| | - Martin Kocher
- Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany
- Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Garry Ceccon
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Cláudia Régio Brambilla
- Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany
- JARA-BRAIN-Translational Medicine, Aachen, Germany
| | - Nadim J Shah
- Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany
- JARA-BRAIN-Translational Medicine, Aachen, Germany
- Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Center for Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
| | - Norbert Galldiks
- Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
| | - Fabian Isensee
- Applied Computer Vision Lab, Helmholtz Imaging, Heidelberg, Germany; and
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine, Forschungszentrum Juelich GmbH, Juelich, Germany;
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Rehman A, Usman M, Shahid A, Latif S, Qadir J. Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation. SENSORS (BASEL, SWITZERLAND) 2023; 23:2346. [PMID: 36850942 PMCID: PMC9964702 DOI: 10.3390/s23042346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Brain tumors are among the deadliest forms of cancer, characterized by abnormal proliferation of brain cells. While early identification of brain tumors can greatly aid in their therapy, the process of manual segmentation performed by expert doctors, which is often time-consuming, tedious, and prone to human error, can act as a bottleneck in the diagnostic process. This motivates the development of automated algorithms for brain tumor segmentation. However, accurately segmenting the enhanced and core tumor regions is complicated due to high levels of inter- and intra-tumor heterogeneity in terms of texture, morphology, and shape. This study proposes a fully automatic method called the selective deeply supervised multi-scale attention network (SDS-MSA-Net) for segmenting brain tumor regions using a multi-scale attention network with novel selective deep supervision (SDS) mechanisms for training. The method utilizes a 3D input composed of five consecutive slices, in addition to a 2D slice, to maintain sequential information. The proposed multi-scale architecture includes two encoding units to extract meaningful global and local features from the 3D and 2D inputs, respectively. These coarse features are then passed through attention units to filter out redundant information by assigning lower weights. The refined features are fed into a decoder block, which upscales the features at various levels while learning patterns relevant to all tumor regions. The SDS block is introduced to immediately upscale features from intermediate layers of the decoder, with the aim of producing segmentations of the whole, enhanced, and core tumor regions. The proposed framework was evaluated on the BraTS2020 dataset and showed improved performance in brain tumor region segmentation, particularly in the segmentation of the core and enhancing tumor regions, demonstrating the effectiveness of the proposed approach. Our code is publicly available.
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Affiliation(s)
- Azka Rehman
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co., Ltd., Seoul 06524, Republic of Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Abdullah Shahid
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co., Ltd., Seoul 06524, Republic of Korea
| | - Siddique Latif
- Faculty of Health, Engineering and Sciences, University of Southern Queensland, Springfield 4300, Australia
| | - Junaid Qadir
- Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
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Vijay S, Guhan T, Srinivasan K, Vincent PMDR, Chang CY. MRI brain tumor segmentation using residual Spatial Pyramid Pooling-powered 3D U-Net. Front Public Health 2023; 11:1091850. [PMID: 36817919 PMCID: PMC9932049 DOI: 10.3389/fpubh.2023.1091850] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
Brain tumor diagnosis has been a lengthy process, and automation of a process such as brain tumor segmentation speeds up the timeline. U-Nets have been a commonly used solution for semantic segmentation, and it uses a downsampling-upsampling approach to segment tumors. U-Nets rely on residual connections to pass information during upsampling; however, an upsampling block only receives information from one downsampling block. This restricts the context and scope of an upsampling block. In this paper, we propose SPP-U-Net where the residual connections are replaced with a combination of Spatial Pyramid Pooling (SPP) and Attention blocks. Here, SPP provides information from various downsampling blocks, which will increase the scope of reconstruction while attention provides the necessary context by incorporating local characteristics with their corresponding global dependencies. Existing literature uses heavy approaches such as the usage of nested and dense skip connections and transformers. These approaches increase the training parameters within the model which therefore increase the training time and complexity of the model. The proposed approach on the other hand attains comparable results to existing literature without changing the number of trainable parameters over larger dimensions such as 160 × 192 × 192. All in all, the proposed model scores an average dice score of 0.883 and a Hausdorff distance of 7.84 on Brats 2021 cross validation.
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Affiliation(s)
- Sanchit Vijay
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Thejineaswar Guhan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - P. M. Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
- Service Systems Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
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