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Fechter T, Sachpazidis I, Baltas D. The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data. Z Med Phys 2024; 34:180-196. [PMID: 36376203 PMCID: PMC11156786 DOI: 10.1016/j.zemedi.2022.10.005] [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: 05/13/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022]
Abstract
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally, we summarised the most recent developments. For better understanding, we provide explanations of key terms and approaches to solving common deep learning problems. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work is on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly present in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. The conclusion of our analysis is that deep learning can positively change the workflow of interventional radiotherapy but there is still room for improvements when it comes to reproducible results and standardised evaluation methods.
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Affiliation(s)
- Tobias Fechter
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany.
| | - Ilias Sachpazidis
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
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Hampole P, Harding T, Gillies D, Orlando N, Edirisinghe C, Mendez LC, D'Souza D, Velker V, Correa R, Helou J, Xing S, Fenster A, Hoover DA. Deep learning-based ultrasound auto-segmentation of the prostate with brachytherapy implanted needles. Med Phys 2024; 51:2665-2677. [PMID: 37888789 DOI: 10.1002/mp.16811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Accurate segmentation of the clinical target volume (CTV) corresponding to the prostate with or without proximal seminal vesicles is required on transrectal ultrasound (TRUS) images during prostate brachytherapy procedures. Implanted needles cause artifacts that may make this task difficult and time-consuming. Thus, previous studies have focused on the simpler problem of segmentation in the absence of needles at the cost of reduced clinical utility. PURPOSE To use a convolutional neural network (CNN) algorithm for segmentation of the prostatic CTV in TRUS images post-needle insertion obtained from prostate brachytherapy procedures to better meet the demands of the clinical procedure. METHODS A dataset consisting of 144 3-dimensional (3D) TRUS images with implanted metal brachytherapy needles and associated manual CTV segmentations was used for training a 2-dimensional (2D) U-Net CNN using a Dice Similarity Coefficient (DSC) loss function. These were split by patient, with 119 used for training and 25 reserved for testing. The 3D TRUS training images were resliced at radial (around the axis normal to the coronal plane) and oblique angles through the center of the 3D image, as well as axial, coronal, and sagittal planes to obtain 3689 2D TRUS images and masks for training. The network generated boundary predictions on 300 2D TRUS images obtained from reslicing each of the 25 3D TRUS images used for testing into 12 radial slices (15° apart), which were then reconstructed into 3D surfaces. Performance metrics included DSC, recall, precision, unsigned and signed volume percentage differences (VPD/sVPD), mean surface distance (MSD), and Hausdorff distance (HD). In addition, we studied whether providing algorithm-predicted boundaries to the physicians and allowing modifications increased the agreement between physicians. This was performed by providing a subset of 3D TRUS images of five patients to five physicians who segmented the CTV using clinical software and repeated this at least 1 week apart. The five physicians were given the algorithm boundary predictions and allowed to modify them, and the resulting inter- and intra-physician variability was evaluated. RESULTS Median DSC, recall, precision, VPD, sVPD, MSD, and HD of the 3D-reconstructed algorithm segmentations were 87.2 [84.1, 88.8]%, 89.0 [86.3, 92.4]%, 86.6 [78.5, 90.8]%, 10.3 [4.5, 18.4]%, 2.0 [-4.5, 18.4]%, 1.6 [1.2, 2.0] mm, and 6.0 [5.3, 8.0] mm, respectively. Segmentation time for a set of 12 2D radial images was 2.46 [2.44, 2.48] s. With and without U-Net starting points, the intra-physician median DSCs were 97.0 [96.3, 97.8]%, and 94.4 [92.5, 95.4]% (p < 0.0001), respectively, while the inter-physician median DSCs were 94.8 [93.3, 96.8]% and 90.2 [88.7, 92.1]%, respectively (p < 0.0001). The median segmentation time for physicians, with and without U-Net-generated CTV boundaries, were 257.5 [211.8, 300.0] s and 288.0 [232.0, 333.5] s, respectively (p = 0.1034). CONCLUSIONS Our algorithm performed at a level similar to physicians in a fraction of the time. The use of algorithm-generated boundaries as a starting point and allowing modifications reduced physician variability, although it did not significantly reduce the time compared to manual segmentations.
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Affiliation(s)
- Prakash Hampole
- Department of Medical Biophysics, Western University, London, ON, Canada
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
| | - Thomas Harding
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
| | - Derek Gillies
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
| | - Nathan Orlando
- Department of Medical Biophysics, Western University, London, ON, Canada
- Robarts Research Institute, Western University, London, ON, Canada
| | | | - Lucas C Mendez
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
| | - David D'Souza
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
| | - Vikram Velker
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
| | - Rohann Correa
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
| | - Joelle Helou
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
| | - Shuwei Xing
- Robarts Research Institute, Western University, London, ON, Canada
- School of Biomedical Engineering, Western University, London, ON, Canada
| | - Aaron Fenster
- Department of Medical Biophysics, Western University, London, ON, Canada
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Medical Imaging, Western University, London, ON, Canada
| | - Douglas A Hoover
- Department of Medical Biophysics, Western University, London, ON, Canada
- Department of Oncology, London Health Sciences Centre, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
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Chen X, Liu X, Wu Y, Wang Z, Wang SH. Research related to the diagnosis of prostate cancer based on machine learning medical images: A review. Int J Med Inform 2024; 181:105279. [PMID: 37977054 DOI: 10.1016/j.ijmedinf.2023.105279] [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: 06/21/2023] [Revised: 09/06/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Prostate cancer is currently the second most prevalent cancer among men. Accurate diagnosis of prostate cancer can provide effective treatment for patients and greatly reduce mortality. The current medical imaging tools for screening prostate cancer are mainly MRI, CT and ultrasound. In the past 20 years, these medical imaging methods have made great progress with machine learning, especially the rise of deep learning has led to a wider application of artificial intelligence in the use of image-assisted diagnosis of prostate cancer. METHOD This review collected medical image processing methods, prostate and prostate cancer on MR images, CT images, and ultrasound images through search engines such as web of science, PubMed, and Google Scholar, including image pre-processing methods, segmentation of prostate gland on medical images, registration between prostate gland on different modal images, detection of prostate cancer lesions on the prostate. CONCLUSION Through these collated papers, it is found that the current research on the diagnosis and staging of prostate cancer using machine learning and deep learning is in its infancy, and most of the existing studies are on the diagnosis of prostate cancer and classification of lesions, and the accuracy is low, with the best results having an accuracy of less than 0.95. There are fewer studies on staging. The research is mainly focused on MR images and much less on CT images, ultrasound images. DISCUSSION Machine learning and deep learning combined with medical imaging have a broad application prospect for the diagnosis and staging of prostate cancer, but the research in this area still has more room for development.
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Affiliation(s)
- Xinyi Chen
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Xiang Liu
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Yuke Wu
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Zhenglei Wang
- Department of Medical Imaging, Shanghai Electric Power Hospital, Shanghai 201620, China.
| | - Shuo Hong Wang
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
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Chen Y, Yu L, Wang JY, Panjwani N, Obeid JP, Liu W, Liu L, Kovalchuk N, Gensheimer MF, Vitzthum LK, Beadle BM, Chang DT, Le QT, Han B, Xing L. Adaptive Region-Specific Loss for Improved Medical Image Segmentation. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:13408-13421. [PMID: 37363838 PMCID: PMC11346301 DOI: 10.1109/tpami.2023.3289667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e., some regions can achieve high prediction performance more easily than others). Here, we introduce a region-specific loss to lift the implicit assumption of homogeneous weighting for better learning. We divide the entire volume into multiple sub-regions, each with an individualized loss constructed for optimal local performance. Effectively, this scheme imposes higher weightings on the sub-regions that are more difficult to segment, and vice versa. Furthermore, the regional false positive and false negative errors are computed for each input image during a training step and the regional penalty is adjusted accordingly to enhance the overall accuracy of the prediction. Using different public and in-house medical image datasets, we demonstrate that the proposed regionally adaptive loss paradigm outperforms conventional methods in the multi-organ segmentations, without any modification to the neural network architecture or additional data preparation.
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Masoumi N, Rivaz H, Hacihaliloglu I, Ahmad MO, Reinertsen I, Xiao Y. The Big Bang of Deep Learning in Ultrasound-Guided Surgery: A Review. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2023; 70:909-919. [PMID: 37028313 DOI: 10.1109/tuffc.2023.3255843] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Ultrasound (US) imaging is a paramount modality in many image-guided surgeries and percutaneous interventions, thanks to its high portability, temporal resolution, and cost-efficiency. However, due to its imaging principles, the US is often noisy and difficult to interpret. Appropriate image processing can greatly enhance the applicability of the imaging modality in clinical practice. Compared with the classic iterative optimization and machine learning (ML) approach, deep learning (DL) algorithms have shown great performance in terms of accuracy and efficiency for US processing. In this work, we conduct a comprehensive review on deep-learning algorithms in the applications of US-guided interventions, summarize the current trends, and suggest future directions on the topic.
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Aydin SG, Bilge HŞ. FPGA Implementation of Image Registration Using Accelerated CNN. SENSORS (BASEL, SWITZERLAND) 2023; 23:6590. [PMID: 37514883 PMCID: PMC10386551 DOI: 10.3390/s23146590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
BACKGROUND Accurate and fast image registration (IR) is critical during surgical interventions where the ultrasound (US) modality is used for image-guided intervention. Convolutional neural network (CNN)-based IR methods have resulted in applications that respond faster than traditional iterative IR methods. However, general-purpose processors are unable to operate at the maximum speed possible for real-time CNN algorithms. Due to its reconfigurable structure and low power consumption, the field programmable gate array (FPGA) has gained prominence for accelerating the inference phase of CNN applications. METHODS This study proposes an FPGA-based ultrasound IR CNN (FUIR-CNN) to regress three rigid registration parameters from image pairs. To speed up the estimation process, the proposed design makes use of fixed-point data and parallel operations carried out by unrolling and pipelining techniques. Experiments were performed on three US datasets in real time using the xc7z020, and the xcku5p was also used during implementation. RESULTS The FUIR-CNN produced results for the inference phase 139 times faster than the software-based network while retaining a negligible drop in regression performance of under 200 MHz clock frequency. CONCLUSIONS Comprehensive experimental results demonstrate that the proposed end-to-end FPGA-based accelerated CNN achieves a negligible loss, a high speed for registration parameters, less power when compared to the CPU, and the potential for real-time medical imaging.
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Affiliation(s)
- Seda Guzel Aydin
- Department of Electrical and Electronics Engineering, Bingol University, Bingol 12000, Turkey
| | - Hasan Şakir Bilge
- Biomedical Calibration and Research Center (BIYOKAM), Gazi University, Ankara 06560, Turkey
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Zhao JZ, Ni R, Chow R, Rink A, Weersink R, Croke J, Raman S. Artificial intelligence applications in brachytherapy: A literature review. Brachytherapy 2023; 22:429-445. [PMID: 37248158 DOI: 10.1016/j.brachy.2023.04.003] [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: 02/02/2023] [Revised: 04/02/2023] [Accepted: 04/07/2023] [Indexed: 05/31/2023]
Abstract
PURPOSE Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field. METHODS AND MATERIALS We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy. RESULTS A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed. CONCLUSIONS AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures.
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Affiliation(s)
- Jonathan Zl Zhao
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Ruiyan Ni
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Ronald Chow
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Alexandra Rink
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Robert Weersink
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Jennifer Croke
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Srinivas Raman
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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Khor HG, Ning G, Sun Y, Lu X, Zhang X, Liao H. Anatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registration. Med Image Anal 2023; 88:102811. [PMID: 37245436 DOI: 10.1016/j.media.2023.102811] [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: 08/19/2022] [Revised: 03/14/2023] [Accepted: 04/04/2023] [Indexed: 05/30/2023]
Abstract
The main objective of anatomically plausible results for deformable image registration is to improve model's registration accuracy by minimizing the difference between a pair of fixed and moving images. Since many anatomical features are closely related to each other, leveraging supervision from auxiliary tasks (such as supervised anatomical segmentation) has the potential to enhance the realism of the warped images after registration. In this work, we employ a Multi-Task Learning framework to formulate registration and segmentation as a joint issue, in which we utilize anatomical constraint from auxiliary supervised segmentation to enhance the realism of the predicted images. First, we propose a Cross-Task Attention Block to fuse the high-level feature from both the registration and segmentation network. With the help of initial anatomical segmentation, the registration network can benefit from learning the task-shared feature correlation and rapidly focusing on the parts that need deformation. On the other hand, the anatomical segmentation discrepancy from ground-truth fixed annotations and predicted segmentation maps of initial warped images are integrated into the loss function to guide the convergence of the registration network. Ideally, a good deformation field should be able to minimize the loss function of registration and segmentation. The voxel-wise anatomical constraint inferred from segmentation helps the registration network to reach a global optimum for both deformable and segmentation learning. Both networks can be employed independently during the testing phase, enabling only the registration output to be predicted when the segmentation labels are unavailable. Qualitative and quantitative results indicate that our proposed methodology significantly outperforms the previous state-of-the-art approaches on inter-patient brain MRI registration and pre- and intra-operative uterus MRI registration tasks within our specific experimental setup, which leads to state-of-the-art registration quality scores of 0.755 and 0.731 (i.e., by 0.8% and 0.5% increases) DSC for both tasks, respectively.
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Affiliation(s)
- Hee Guan Khor
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Guochen Ning
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yihua Sun
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xu Lu
- Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong, China
| | - Xinran Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Hongen Liao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
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Orlando N, Edirisinghe C, Gyacskov I, Vickress J, Sachdeva R, Gomez JA, D'Souza D, Velker V, Mendez LC, Bauman G, Fenster A, Hoover DA. Validation of a surface-based deformable MRI-3D ultrasound image registration algorithm toward clinical implementation for interstitial prostate brachytherapy. Brachytherapy 2023; 22:199-209. [PMID: 36641305 DOI: 10.1016/j.brachy.2022.11.011] [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: 06/21/2022] [Revised: 11/05/2022] [Accepted: 11/28/2022] [Indexed: 01/13/2023]
Abstract
PURPOSE The purpose of this study was to evaluate and clinically implement a deformable surface-based magnetic resonance imaging (MRI) to three-dimensional ultrasound (US) image registration algorithm for prostate brachytherapy (BT) with the aim to reduce operator dependence and facilitate dose escalation to an MRI-defined target. METHODS AND MATERIALS Our surface-based deformable image registration (DIR) algorithm first translates and scales to align the US- and MR-defined prostate surfaces, followed by deformation of the MR-defined prostate surface to match the US-defined prostate surface. The algorithm performance was assessed in a phantom using three deformation levels, followed by validation in three retrospective high-dose-rate BT clinical cases. For comparison, manual rigid registration and cognitive fusion by physician were also employed. Registration accuracy was assessed using the Dice similarity coefficient (DSC) and target registration error (TRE) for embedded spherical landmarks. The algorithm was then implemented intraoperatively in a prospective clinical case. RESULTS In the phantom, our DIR algorithm demonstrated a mean DSC and TRE of 0.74 ± 0.08 and 0.94 ± 0.49 mm, respectively, significantly improving the performance compared to manual rigid registration with 0.64 ± 0.16 and 1.88 ± 1.24 mm, respectively. Clinical results demonstrated reduced variability compared to the current standard of cognitive fusion by physicians. CONCLUSIONS We successfully validated a DIR algorithm allowing for translation of MR-defined target and organ-at-risk contours into the intraoperative environment. Prospective clinical implementation demonstrated the intraoperative feasibility of our algorithm, facilitating targeted biopsies and dose escalation to the MR-defined lesion. This method provides the potential to standardize the registration procedure between physicians, reducing operator dependence.
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Affiliation(s)
- Nathan Orlando
- Department of Medical Biophysics, Western University, London, Ontario, Canada; Robarts Research Institute, Western University, London, Ontario, Canada.
| | | | - Igor Gyacskov
- Robarts Research Institute, Western University, London, Ontario, Canada
| | - Jason Vickress
- Department of Oncology, Western University, London, Ontario, Canada; London Health Sciences Centre, London, Ontario, Canada
| | - Robin Sachdeva
- Lawson Health Research Institute, London, Ontario, Canada
| | - Jose A Gomez
- London Health Sciences Centre, London, Ontario, Canada; Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - David D'Souza
- Department of Oncology, Western University, London, Ontario, Canada; London Health Sciences Centre, London, Ontario, Canada
| | - Vikram Velker
- Department of Oncology, Western University, London, Ontario, Canada; London Health Sciences Centre, London, Ontario, Canada
| | - Lucas C Mendez
- Department of Oncology, Western University, London, Ontario, Canada; London Health Sciences Centre, London, Ontario, Canada
| | - Glenn Bauman
- Department of Oncology, Western University, London, Ontario, Canada; London Health Sciences Centre, London, Ontario, Canada
| | - Aaron Fenster
- Department of Medical Biophysics, Western University, London, Ontario, Canada; Robarts Research Institute, Western University, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada
| | - Douglas A Hoover
- Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada; London Health Sciences Centre, London, Ontario, Canada
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Ruthven M, Miquel ME, King AP. A segmentation-informed deep learning framework to register dynamic two-dimensional magnetic resonance images of the vocal tract during speech. Biomed Signal Process Control 2023; 80:104290. [PMID: 36743699 PMCID: PMC9746295 DOI: 10.1016/j.bspc.2022.104290] [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: 08/04/2022] [Revised: 09/29/2022] [Accepted: 10/08/2022] [Indexed: 11/06/2022]
Abstract
Objective Dynamic magnetic resonance (MR) imaging enables visualisation of articulators during speech. There is growing interest in quantifying articulator motion in two-dimensional MR images of the vocal tract, to better understand speech production and potentially inform patient management decisions. Image registration is an established way to achieve this quantification. Recently, segmentation-informed deformable registration frameworks have been developed and have achieved state-of-the-art accuracy. This work aims to adapt such a framework and optimise it for estimating displacement fields between dynamic two-dimensional MR images of the vocal tract during speech. Methods A deep-learning-based registration framework was developed and compared with current state-of-the-art registration methods and frameworks (two traditional methods and three deep-learning-based frameworks, two of which are segmentation informed). The accuracy of the methods and frameworks was evaluated using the Dice coefficient (DSC), average surface distance (ASD) and a metric based on velopharyngeal closure. The metric evaluated if the fields captured a clinically relevant and quantifiable aspect of articulator motion. Results The segmentation-informed frameworks achieved higher DSCs and lower ASDs and captured more velopharyngeal closures than the traditional methods and the framework that was not segmentation informed. All segmentation-informed frameworks achieved similar DSCs and ASDs. However, the proposed framework captured the most velopharyngeal closures. Conclusions A framework was successfully developed and found to more accurately estimate articulator motion than five current state-of-the-art methods and frameworks. Significance The first deep-learning-based framework specifically for registering dynamic two-dimensional MR images of the vocal tract during speech has been developed and evaluated.
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Affiliation(s)
- Matthieu Ruthven
- Clinical Physics, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom,School of Biomedical Engineering & Imaging Sciences, King’s College London, King’s Health Partners, St Thomas’ Hospital, London SE1 7EH, United Kingdom,Corresponding author at: Clinical Physics, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom.
| | - Marc E. Miquel
- Clinical Physics, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom,Digital Environment Research Institute (DERI), Empire House, 67-75 New Road, Queen Mary University of London, London E1 1HH, United Kingdom,Advanced Cardiovascular Imaging, Barts NIHR BRC, Queen Mary University of London, London EC1M 6BQ, United Kingdom
| | - Andrew P. King
- School of Biomedical Engineering & Imaging Sciences, King’s College London, King’s Health Partners, St Thomas’ Hospital, London SE1 7EH, United Kingdom
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12
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Li H, Lee CH, Chia D, Lin Z, Huang W, Tan CH. Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities. Diagnostics (Basel) 2022; 12:diagnostics12020289. [PMID: 35204380 PMCID: PMC8870978 DOI: 10.3390/diagnostics12020289] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/31/2021] [Accepted: 01/14/2022] [Indexed: 02/04/2023] Open
Abstract
Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.
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Affiliation(s)
- Huanye Li
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (H.L.); (Z.L.)
| | - Chau Hung Lee
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore;
| | - David Chia
- Department of Radiation Oncology, National University Cancer Institute (NUH), Singapore 119074, Singapore;
| | - Zhiping Lin
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (H.L.); (Z.L.)
| | - Weimin Huang
- Institute for Infocomm Research, A*Star, Singapore 138632, Singapore;
| | - Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore;
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798, Singapore
- Correspondence:
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Song WY, Robar JL, Morén B, Larsson T, Carlsson Tedgren Å, Jia X. Emerging technologies in brachytherapy. Phys Med Biol 2021; 66. [PMID: 34710856 DOI: 10.1088/1361-6560/ac344d] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 10/28/2021] [Indexed: 01/15/2023]
Abstract
Brachytherapy is a mature treatment modality. The literature is abundant in terms of review articles and comprehensive books on the latest established as well as evolving clinical practices. The intent of this article is to part ways and look beyond the current state-of-the-art and review emerging technologies that are noteworthy and perhaps may drive the future innovations in the field. There are plenty of candidate topics that deserve a deeper look, of course, but with practical limits in this communicative platform, we explore four topics that perhaps is worthwhile to review in detail at this time. First, intensity modulated brachytherapy (IMBT) is reviewed. The IMBT takes advantage ofanisotropicradiation profile generated through intelligent high-density shielding designs incorporated onto sources and applicators such to achieve high quality plans. Second, emerging applications of 3D printing (i.e. additive manufacturing) in brachytherapy are reviewed. With the advent of 3D printing, interest in this technology in brachytherapy has been immense and translation swift due to their potential to tailor applicators and treatments customizable to each individual patient. This is followed by, in third, innovations in treatment planning concerning catheter placement and dwell times where new modelling approaches, solution algorithms, and technological advances are reviewed. And, fourth and lastly, applications of a new machine learning technique, called deep learning, which has the potential to improve and automate all aspects of brachytherapy workflow, are reviewed. We do not expect that all ideas and innovations reviewed in this article will ultimately reach clinic but, nonetheless, this review provides a decent glimpse of what is to come. It would be exciting to monitor as IMBT, 3D printing, novel optimization algorithms, and deep learning technologies evolve over time and translate into pilot testing and sensibly phased clinical trials, and ultimately make a difference for cancer patients. Today's fancy is tomorrow's reality. The future is bright for brachytherapy.
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Affiliation(s)
- William Y Song
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - James L Robar
- Department of Radiation Oncology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Björn Morén
- Department of Mathematics, Linköping University, Linköping, Sweden
| | - Torbjörn Larsson
- Department of Mathematics, Linköping University, Linköping, Sweden
| | - Åsa Carlsson Tedgren
- Radiation Physics, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden.,Department of Oncology Pathology, Karolinska Institute, Stockholm, Sweden
| | - Xun Jia
- Innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
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