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Morris DM, Wang C, Papanastasiou G, Gray CD, Xu W, Sjöström S, Badr S, Paccou J, Semple SIK, MacGillivray T, Cawthorn WP. A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data. Comput Struct Biotechnol J 2024; 24:89-104. [PMID: 38268780 PMCID: PMC10806280 DOI: 10.1016/j.csbj.2023.12.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/26/2024] Open
Abstract
Background Bone marrow adipose tissue (BMAT) represents > 10% fat mass in healthy humans and can be measured by magnetic resonance imaging (MRI) as the bone marrow fat fraction (BMFF). Human MRI studies have identified several diseases associated with BMFF but have been relatively small scale. Population-scale studies therefore have huge potential to reveal BMAT's true clinical relevance. The UK Biobank (UKBB) is undertaking MRI of 100,000 participants, providing the ideal opportunity for such advances. Objective To establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data. Materials and methods We studied males and females aged 60-69. Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. Using manual segmentations from 61-64 subjects, the models were trained to segment four BM regions of interest: the spine (thoracic and lumbar vertebrae), femoral head, total hip and femoral diaphysis. Models were tested using a further 10-12 datasets per region and validated using datasets from 729 UKBB participants. BMFF was then quantified and pathophysiological characteristics assessed, including site- and sex-dependent differences and the relationships with age, BMI, bone mineral density, peripheral adiposity, and osteoporosis. Results Model accuracy matched or exceeded that for conventional U-Nets, yielding Dice scores of 91.2% (spine), 94.5% (femoral head), 91.2% (total hip) and 86.6% (femoral diaphysis). One case of severe scoliosis prevented segmentation of the spine, while one case of Non-Hodgkin Lymphoma prevented segmentation of the spine, femoral head and total hip because of T2 signal depletion; however, successful segmentation was not disrupted by any other pathophysiological variables. The resulting BMFF measurements confirmed expected relationships between BMFF and age, sex and bone density, and identified new site- and sex-specific characteristics. Conclusions We have established a new deep learning method for accurate segmentation of small structures from large volumetric data, allowing high-throughput multi-site BMFF measurement in the UKBB. Our findings reveal new pathophysiological insights, highlighting the potential of BMFF as a novel clinical biomarker. Applying our method across the full UKBB cohort will help to reveal the impact of BMAT on human health and disease.
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Affiliation(s)
- David M. Morris
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Chengjia Wang
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- School of Mathematics and Computer Sciences, Heriot-Watt University, Edinburgh EH14 1AS, UK
| | - Giorgos Papanastasiou
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- School of Computer Science and Electronic Engineering, Wivenhoe Park, The University of Essex, Colchester CO4 3SQ, UK
| | - Calum D. Gray
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Wei Xu
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Samuel Sjöström
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Sammy Badr
- University of Lille, Marrow Adiposity and Bone Laboratory (MABlab) ULR 4490, F-59000 Lille, France
- CHU Lille, Department of Radiology and Musculoskeletal Imaging, F-59000 Lille, France
| | - Julien Paccou
- University of Lille, Marrow Adiposity and Bone Laboratory (MABlab) ULR 4490, F-59000 Lille, France
- CHU Lille, Department of Rheumatology, F-59000 Lille, France
| | - Scott IK Semple
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Tom MacGillivray
- Centre for Clinical Brain Sciences, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - William P. Cawthorn
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
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Giraudo C, Sudol-Szopinska I, Fichera G, Evangelista L, Zanatta E, Del Grande F, Stramare R, Bazzocchi A, Guglielmi G, Rennie W. Update on Rheumatic Diseases in Clinical Practice: Recent Concepts and Developments. Radiol Clin North Am 2024; 62:725-738. [PMID: 39059968 DOI: 10.1016/j.rcl.2024.02.005] [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] [Indexed: 07/28/2024]
Abstract
Diagnostic imaging is essential in the diagnostic process of rheumatic diseases. Given the heterogeneity of this group of diseases and the tremendous impact of novel therapeutic options, guidelines and recommendations regarding the optimal choice of the most appropriate technique/s are continuously revised and radiologists should always be up-to-date. Last, because of the continuous technological innovations, we will assist to the progressive application of advanced techniques and tools in rheumatology.
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Affiliation(s)
- Chiara Giraudo
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health - DCTV, University of Padova, Via Giustiniani 2, Padova, 35122, Italy.
| | - Iwona Sudol-Szopinska
- Department of Radiology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 1- Spartanska Street, Warsaw, Poland
| | - Giulia Fichera
- Pediatric Radiology, Padova Hospital, Via Giustiniani 2, Padova, 35122, Italy
| | - Laura Evangelista
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan, 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan, 20089, Italy
| | - Elisabetta Zanatta
- Department of Medicine -DIMED, Rheumatology Unit, University of Padova, Via Giustiniani 2, 35122, Padova, Italy
| | - Filippo Del Grande
- Istituto Di Imaging Della Svizzera Italiana (IIMSI), Clinica Di Radiologia Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, Lugano 6900, Switzerland
| | - Roberto Stramare
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health - DCTV, University of Padova, Via Giustiniani 2, Padova, 35122, Italy
| | - Alberto Bazzocchi
- Department of Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Via G. C. Pupilli 1, Bologna, 40136, Italy
| | - Giuseppe Guglielmi
- Department of Clinical and Experimental Medicine, Radiology Unit, Foggia University School of Medicine, Via Gramsci 89, 71122, Foggia, Italy; Department of Radiology, Scientific Institute "Casa Sollievo Della Sofferenza" Hospital, Viale Cappuccini, San Giovanni Rotondo, 71013, Italy
| | - Winston Rennie
- Department of Radiology, Leicester Royal Infirmary, Infirmary Square, Leicester, LE1 5WW, United Kingdom
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Stirling CE, Neeteson NJ, Walker REA, Boyd SK. Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear. Comput Biol Med 2024; 178:108791. [PMID: 38905892 DOI: 10.1016/j.compbiomed.2024.108791] [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: 02/01/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 06/23/2024]
Abstract
INTRODUCTION Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with regions of elevated localized bone loss, and studies suggest these may act as a precursor to the development of post-traumatic osteoarthritis. This study addresses the labour-intensive manual assessment of BMLs by using a 3D U-Net for automated identification and segmentation from MRI scans. METHODS A multi-task learning approach was used to segment both bone and BML from T2 fat-suppressed (FS) fast spin echo (FSE) MRI sequences for BML assessment. Training and testing utilized datasets from individuals with complete ACL tears, employing a five-fold cross-validation approach and pre-processing involved image intensity normalization and data augmentation. A post-processing algorithm was developed to improve segmentation and remove outliers. Training and testing datasets were acquired from different studies with similar imaging protocol to assess the model's performance robustness across different populations and acquisition conditions. RESULTS The 3D U-Net model exhibited effectiveness in semantic segmentation, while post-processing enhanced segmentation accuracy and precision through morphological operations. The trained model with post-processing achieved a Dice similarity coefficient (DSC) of 0.75 ± 0.08 (mean ± std) and a precision of 0.87 ± 0.07 for BML segmentation on testing data. Additionally, the trained model with post-processing achieved a DSC of 0.93 ± 0.02 and a precision of 0.92 ± 0.02 for bone segmentation on testing data. This demonstrates the approach's high accuracy for capturing true positives and effectively minimizing false positives in the identification and segmentation of bone structures. CONCLUSION Automated segmentation methods are a valuable tool for clinicians and researchers, streamlining the assessment of BMLs and allowing for longitudinal assessments. This study presents a model with promising clinical efficacy and provides a quantitative approach for bone-related pathology research and diagnostics.
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Affiliation(s)
- Callie E Stirling
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Nathan J Neeteson
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Richard E A Walker
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Steven K Boyd
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
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Yu AS, Yang M, Lartey R, Holden W, Ok AH, Khan S, Kim J, Winalski C, Subhas N, Chaudhary V, Li X. Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models. Bioengineering (Basel) 2024; 11:526. [PMID: 38927762 PMCID: PMC11200419 DOI: 10.3390/bioengineering11060526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/07/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024] Open
Abstract
Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to quantify the significance of BMELs. However, their utilization is hampered by the labor-intensive and time-consuming nature of the process as well as by annotator bias, especially since BMELs exhibit various sizes and irregular shapes with diffuse signal that lead to poor intra- and inter-rater reliability. In this study, we propose a novel unsupervised method for fully automated segmentation of BMELs that leverages conditional diffusion models, multiple MRI sequences that have different contrast of BMELs, and anomaly detection that do not rely on costly and error-prone annotations. We also analyze BMEL segmentation annotations from multiple experts, reporting intra-/inter-rater variability and setting better benchmarks for BMEL segmentation performance.
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Affiliation(s)
- Andrew Seohwan Yu
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Mingrui Yang
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Richard Lartey
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - William Holden
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Ahmet Hakan Ok
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Sameed Khan
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jeehun Kim
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Carl Winalski
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Naveen Subhas
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Vipin Chaudhary
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Xiaojuan Li
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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Forestieri M, Napolitano A, Tomà P, Bascetta S, Cirillo M, Tagliente E, Fracassi D, D’Angelo P, Casazza I. Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI. Diagnostics (Basel) 2023; 14:61. [PMID: 38201370 PMCID: PMC10804385 DOI: 10.3390/diagnostics14010061] [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: 10/24/2023] [Revised: 12/17/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
OBJECTIVE The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis. MATERIALS AND METHODS We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.5 T MRI scanner. Using the opensource 3D Slicer software version 4.10.2, the ROIs on CNO lesions and on the red bone marrow were sampled. Texture analysis (TA) was carried out using Pyradiomics. We applied an optimization search grid algorithm on nine classic ML classifiers and a Deep Learning (DL) Neural Network (NN). The model's performance was evaluated using Accuracy (ACC), AUC-ROC curves, F1-score, Positive Predictive Value (PPV), Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). Furthermore, we used Shapley additive explanations to gain insight into the behavior of the prediction model. RESULTS Most predictive characteristics were selected by Boruta algorithm for each combination of ROI sequences for the characterization and classification of the two types of signal hyperintensity. The overall best classification result was obtained by the NN with ACC = 0.91, AUC = 0.93 with 95% CI 0.91-0.94, F1-score = 0.94 and PPV = 93.8%. Between classic ML methods, ensemble learners showed high model performance; specifically, the best-performing classifier was the Stack (ST) with ACC = 0.85, AUC = 0.81 with 95% CI 0.8-0.84, F1-score = 0.9, PPV = 90%. CONCLUSIONS Our results show the potential of ML methods in discerning edema-like lesions, in particular by distinguishing CNO lesions from hematopoietic bone marrow changes in a pediatric population. The Neural Network showed the overall best results, while a Stacking classifier, based on Gradient Boosting and Random Forest as principal estimators and Logistic Regressor as final estimator, achieved the best results between the other ML methods.
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Affiliation(s)
- Marta Forestieri
- Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (P.T.); (S.B.); (P.D.); (I.C.)
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (A.N.); (E.T.); (D.F.)
| | - Paolo Tomà
- Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (P.T.); (S.B.); (P.D.); (I.C.)
| | - Stefano Bascetta
- Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (P.T.); (S.B.); (P.D.); (I.C.)
| | - Marco Cirillo
- Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (P.T.); (S.B.); (P.D.); (I.C.)
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (A.N.); (E.T.); (D.F.)
| | - Donatella Fracassi
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (A.N.); (E.T.); (D.F.)
| | - Paola D’Angelo
- Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (P.T.); (S.B.); (P.D.); (I.C.)
| | - Ines Casazza
- Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (P.T.); (S.B.); (P.D.); (I.C.)
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Qin C, Zheng B, Li W, Chen H, Zeng J, Wu C, Liang S, Luo J, Zhou S, Xiao L. MAD-Net: Multi-attention dense network for functional bone marrow segmentation. Comput Biol Med 2023; 154:106428. [PMID: 36682178 DOI: 10.1016/j.compbiomed.2022.106428] [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: 06/02/2022] [Revised: 12/04/2022] [Accepted: 12/13/2022] [Indexed: 01/15/2023]
Abstract
Radiotherapy is the main treatment modality for various pelvic malignancies. However, high intensity radiation can damage the functional bone marrow (FBM), resulting in hematological toxicity (HT). Accurate identification and protection of the FBM during radiotherapy planning can reduce pelvic HT. The traditional manual method for contouring the FBM is time-consuming and laborious. Therefore, development of an efficient and accurate automatic segmentation mode can provide a distinct leverage in clinical settings. In this paper, we propose the first network for performing the FBM segmentation task, which is referred to as the multi-attention dense network (named MAD-Net). Primarily, we introduce the dense convolution block to promote the gradient flow in the network as well as incite feature reuse. Next, a novel slide-window attention module is proposed to emphasize long-range dependencies and exploit interdependencies between features. Finally, we design a residual-dual attention module as the bottleneck layer, which further aggregates useful spatial details and explores intra-class responsiveness of high-level features. In this work, we conduct extensive experiments on our dataset of 3838 two-dimensional pelvic slices. Experimental results demonstrate that the proposed MAD-Net transcends previous state-of-the-art models in various metrics. In addition, the contributions of the proposed components are verified by ablation analysis, and we conduct experiments on three other datasets to manifest the generalizability of MAD-Net.
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Affiliation(s)
- Chuanbo Qin
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China
| | - Bin Zheng
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China
| | - Wanying Li
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China
| | - Hongbo Chen
- Radiotherapy Center, Jiangmen Central Hospital, Jiangmen, 529020, China
| | - Junying Zeng
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China
| | - Chenwang Wu
- Radiotherapy Center, Jiangmen Central Hospital, Jiangmen, 529020, China
| | - Shufen Liang
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China
| | - Jun Luo
- School of Economics and Management, Wuyi University, Jiangmen, 529020, China
| | - Shuquan Zhou
- Radiotherapy Center, Jiangmen Central Hospital, Jiangmen, 529020, China
| | - Lin Xiao
- Radiotherapy Center, Jiangmen Central Hospital, Jiangmen, 529020, China.
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ElGendy K, Barwick TD, Auner HW, Chaidos A, Wallitt K, Sergot A, Rockall A. Repeatability and test-retest reproducibility of mean apparent diffusion coefficient measurements of focal and diffuse disease in relapsed multiple myeloma at 3T whole body diffusion-weighted MRI (WB-DW-MRI). Br J Radiol 2022; 95:20220418. [PMID: 35867890 PMCID: PMC9815750 DOI: 10.1259/bjr.20220418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVE To assess the test-retest reproducibility and intra/interobserver agreement of apparent diffusion coefficient (ADC) measurements of myeloma lesions using whole body diffusion-weighted MRI (WB-DW-MRI) at 3T MRI. METHODS Following ethical approval, 11 consenting patients with relapsed multiple myeloma were prospectively recruited and underwent baseline WB-DW-MRI. For a single bed position, axial DWI was repeated after a short interval to permit test-retest measurements.Mean ADC measurement was performed by two experienced observers. Intra- and interobserver agreement and test-retest reproducibility were assessed, using coefficient of variation (CV) and interclass correlation coefficient (ICC) measures, for diffuse and focal lesions (small ≤10 mm and large >10 mm). RESULTS 47 sites of disease were outlined (23 focal, 24 diffuse) in different bed positions (pelvis = 22, thorax = 20, head and neck = 5). For all lesions, there was excellent intraobserver agreement with ICC of 0.99 (0.98-0.99) and COV of 5%. For interobserver agreement, ICC was 0.89 (0.8-0.934) and COV was 17%. There was poor interobserver agreement for diffuse disease (ICC = 0.46) and small lesions (ICC = 0.54).For test-retest reproducibility, excellent ICC (0.916) and COV (14.5%) values for mean ADC measurements were observed. ICCs of test-retest were similar between focal lesions (0.83) and diffuse infiltration (0.80), while ICCs were higher in pelvic (0.95) compared to thoracic (0.81) region and in small (0.96) compared to large (0.8) lesions. CONCLUSION ADC measurements of focal lesions in multiple myeloma are repeatable and reproducible, while there is more variation in ADC measurements of diffuse disease in patients with multiple myeloma. ADVANCES IN KNOWLEDGE Mean ADC measurements are repeatable and reproducible in focal lesions in multiple myeloma, while the ADC measurements of diffuse disease in multiple myeloma are more subject to variation. The evidence supports the future potential role of ADC measurements as predictive quantitative biomarker in multiple myeloma.
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Affiliation(s)
| | | | | | | | - Kathryn Wallitt
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Antoni Sergot
- Imperial College Healthcare NHS Trust, London, United Kingdom
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