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The impact of data augmentation and transfer learning on the performance of deep learning models for the segmentation of the hip on 3D magnetic resonance images

Montin, Eros; Deniz, Cem M.; Kijowski, Richard; Youm, Thomas; Lattanzi, Riccardo
Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation, and planning of surgical treatments. These models can be generated by building 3D surfaces of the joint's structures segmented on magnetic resonance (MR) images. Deep learning can avoid time-consuming manual segmentations, but its performance depends on the amount and quality of the available training data. Data augmentation and transfer learning are two approaches used when there is only a limited number of datasets. In particular, data augmentation can be used to artificially increase the size and diversity of the training datasets, whereas transfer learning can be used to build the desired model on top of a model previously trained with similar data. This study investigates the effect of data augmentation and transfer learning on the performance of deep learning for the automatic segmentation of the femur and acetabulum on 3D MR images of patients diagnosed with femoroacetabular impingement. Transfer learning was applied starting from a model trained for the segmentation of the bony structures of the shoulder joint, which bears some resemblance to the hip joint. Our results suggest that data augmentation is more effective than transfer learning, yielding a Dice similarity coefficient compared to ground-truth manual segmentations of 0.84 and 0.89 for the acetabulum and femur, respectively, whereas the Dice coefficient was 0.78 and 0.88 for the model based on transfer learning. The Accuracy for the two anatomical regions was 0.95 and 0.97 when using data augmentation, and 0.87 and 0.96 when using transfer learning. Data augmentation can improve the performance of deep learning models by increasing the diversity of the training dataset and making the models more robust to noise and variations in image quality. The proposed segmentation model could be combined with radiomic analysis for the automatic evaluation of hip pathologies.
SCOPUS:85182365313
ISSN: 2352-9148
CID: 5629782

Deep learning applications in osteoarthritis imaging

Kijowski, Richard; Fritz, Jan; Deniz, Cem M
Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI. A variety of DL methods have been developed for detecting and grading the severity of knee OA and various features of knee OA on X-rays using standardized classification systems with diagnostic performance similar to human readers. Multiple DL approaches have been described for fully automated segmentation of cartilage and other knee tissues and have achieved higher segmentation accuracy than currently used methods with substantial reductions in segmentation times. Various DL models analyzing baseline X-rays and MRI have been developed for OA risk assessment. These models have shown high diagnostic performance for predicting a wide variety of OA outcomes, including the incidence and progression of radiographic knee OA, the presence and progression of knee pain, and future total knee replacement. The preliminary results of DL applications in OA imaging have been encouraging. However, many DL techniques require further technical refinement to maximize diagnostic performance. Furthermore, the generalizability of DL approaches needs to be further investigated in prospective studies using large image datasets acquired at different institutions with different imaging hardware before they can be implemented in clinical practice and research studies.
PMCID:10409879
PMID: 36759367
ISSN: 1432-2161
CID: 5626272

Prediction of total knee replacement using deep learning analysis of knee MRI

Rajamohan, Haresh Rengaraj; Wang, Tianyu; Leung, Kevin; Chang, Gregory; Cho, Kyunghyun; Kijowski, Richard; Deniz, Cem M
Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA to total knee replacement (TKR) over a 108-month follow-up period using baseline knee MRI. Participants of our retrospective study consisted of 353 case-control pairs of subjects from the Osteoarthritis Initiative with and without TKR over a 108-month follow-up period matched according to age, sex, ethnicity, and body mass index. A traditional risk assessment model was created to predict TKR using baseline clinical risk factors. DL models were created to predict TKR using baseline knee radiographs and MRI. All DL models had significantly higher (p < 0.001) AUCs than the traditional model. The MRI and radiograph ensemble model and MRI ensemble model (where TKR risk predicted by several contrast-specific DL models were averaged to get the ensemble TKR risk prediction) had the highest AUCs of 0.90 (80% sensitivity and 85% specificity) and 0.89 (79% sensitivity and 86% specificity), respectively, which were significantly higher (p < 0.05) than the AUCs of the radiograph and multiple MRI models (where the DL models were trained to predict TKR risk using single contrast or 2 contrasts together as input). DL models using baseline MRI had a higher diagnostic performance for predicting TKR than a traditional model using baseline clinical risk factors and a DL model using baseline knee radiographs.
PMCID:10147603
PMID: 37117260
ISSN: 2045-2322
CID: 5465642

The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Hirvasniemi, J; Runhaar, J; van der Heijden, R A; Zokaeinikoo, M; Yang, M; Li, X; Tan, J; Rajamohan, H R; Zhou, Y; Deniz, C M; Caliva, F; Iriondo, C; Lee, J J; Liu, F; Martinez, A M; Namiri, N; Pedoia, V; Panfilov, E; Bayramoglu, N; Nguyen, H H; Nieminen, M T; Saarakkala, S; Tiulpin, A; Lin, E; Li, A; Li, V; Dam, E B; Chaudhari, A S; Kijowski, R; Bierma-Zeinstra, S; Oei, E H G; Klein, S
OBJECTIVES/OBJECTIVE:The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. DESIGN/METHODS:The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). RESULTS:Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. CONCLUSION/CONCLUSIONS:The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.
PMID: 36243308
ISSN: 1522-9653
CID: 5361322

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Desai, Arjun D; Caliva, Francesco; Iriondo, Claudia; Mortazi, Aliasghar; Jambawalikar, Sachin; Bagci, Ulas; Perslev, Mathias; Igel, Christian; Dam, Erik B; Gaj, Sibaji; Yang, Mingrui; Li, Xiaojuan; Deniz, Cem M; Juras, Vladimir; Regatte, Ravinder; Gold, Garry E; Hargreaves, Brian A; Pedoia, Valentina; Chaudhari, Akshay S
Purpose/UNASSIGNED:To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Materials and Methods/UNASSIGNED:A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Similarities in automated segmentations were measured using pairwise Dice coefficient correlations. Articular cartilage thickness was computed longitudinally and with scans. Correlation between thickness error and segmentation metrics was measured using the Pearson correlation coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results/UNASSIGNED:= .99). Empirical upper-bound performances were similar for both combinations (P = .99). Conclusion/UNASSIGNED:
PMCID:8231759
PMID: 34235438
ISSN: 2638-6100
CID: 4937462

Specific absorption rate implications of within-scan patient head motion for ultra-high field MRI

Kopanoglu, Emre; Deniz, Cem M; Erturk, M Arcan; Wise, Richard G
PURPOSE/OBJECTIVE:This study investigates the implications of all degrees of freedom of within-scan patient head motion on patient safety. METHODS:Electromagnetic simulations were performed by displacing and/or rotating a virtual body model inside an 8-channel transmit array to simulate 6 degrees of freedom of motion. Rotations of up to 20° and displacements of up to 20 mm including off-axis axial/coronal translations were investigated, yielding 104 head positions. Quadrature excitation, RF shimming, and multi-spoke parallel-transmit excitation pulses were designed for axial slice-selection at 7T, for seven slices across the head. Variation of whole-head specific absorption rate (SAR) and 10-g averaged local SAR of the designed pulses, as well as the change in the maximum eigenvalue (worst-case pulse) were investigated by comparing off-center positions to the central position. RESULTS:In their respective worst-cases, patient motion increased the eigenvalue-based local SAR by 42%, whole-head SAR by 60%, and the 10-g averaged local SAR by 210%. Local SAR was observed to be more sensitive to displacements along right-left and anterior-posterior directions than displacement in the superior-inferior direction and rotation. CONCLUSION/CONCLUSIONS:This is the first study to investigate the effect of all 6 degrees of freedom of motion on safety of practical pulses. Although the results agree with the literature for overlapping cases, the results demonstrate higher increases (up to 3.1-fold) in local SAR for off-axis displacement in the axial plane, which had received less attention in the literature. This increase in local SAR could potentially affect the local SAR compliance of subjects, unless realistic within-scan patient motion is taken into account during pulse design.
PMID: 32301177
ISSN: 1522-2594
CID: 4401792

Three-dimensional MRI Bone Models of the Glenohumeral Joint Using Deep Learning: Evaluation of Normal Anatomy and Glenoid Bone Loss

Cantarelli Rodrigues, Tatiane; Deniz, Cem M; Alaia, Erin F; Gorelik, Natalia; Babb, James S; Dublin, Jared; Gyftopoulos, Soterios
Purpose/UNASSIGNED:To use convolutional neural networks (CNNs) for fully automated MRI segmentation of the glenohumeral joint and evaluate the accuracy of three-dimensional (3D) MRI models created with this method. Materials and Methods/UNASSIGNED:Shoulder MR images of 100 patients (average age, 44 years; range, 14-80 years; 60 men) were retrospectively collected from September 2013 to August 2018. CNNs were used to develop a fully automated segmentation model for proton density-weighted images. Shoulder MR images from an additional 50 patients (mean age, 33 years; range, 16-65 years; 35 men) were retrospectively collected from May 2014 to April 2019 to create 3D MRI glenohumeral models by transfer learning using Dixon-based sequences. Two musculoskeletal radiologists performed measurements on fully and semiautomated segmented 3D MRI models to assess glenohumeral anatomy, glenoid bone loss (GBL), and their impact on treatment selection. Performance of the CNNs was evaluated using Dice similarity coefficient (DSC), sensitivity, precision, and surface-based distance measurements. Measurements were compared using matched-pairs Wilcoxon signed rank test. Results/UNASSIGNED:value range, .097-.99). Conclusion/UNASSIGNED:© RSNA, 2020.
PMCID:7529433
PMID: 33033803
ISSN: 2638-6100
CID: 4627252

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative

Leung, Kevin; Zhang, Bofei; Tan, Jimin; Shen, Yiqiu; Geras, Krzysztof J; Babb, James S; Cho, Kyunghyun; Chang, Gregory; Deniz, Cem M
Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). Conclusion The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Richardson in this issue.
PMID: 32573386
ISSN: 1527-1315
CID: 4492992

Attention-based CNN for KL Grade Classification: Data from the Osteoarthritis Initiative [Meeting Abstract]

Zhang, Bofei; Tan, Jimin; Cho, Kyunghyun; Chang, Gregory; Deniz, Cem M.
ISI:000578080300143
ISSN: 1945-7928
CID: 4661742

Semi-supervised Learning for Predicting Total Knee Replacement with Unsupervised Data Augmentation [Meeting Abstract]

Tan, Jimin; Zhang, Bofei; Cho, Kyunghyun; Chang, Gregory; Deniz, Cem M.
ISI:000582673400022
ISSN: 0277-786x
CID: 4688692