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Goto M, Otsuka Y, Hagiwara A, Fujita S, Hori M, Kamagata K, Aoki S, Abe O, Sakamoto H, Sakano Y, Kyogoku S, Daida H. Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images. Radiol Phys Technol 2023; 16:373-383. [PMID: 37291372 DOI: 10.1007/s12194-023-00728-z] [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: 03/10/2023] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 06/10/2023]
Abstract
In automated analyses of brain morphometry, skull stripping or brain extraction is a critical first step because it provides accurate spatial registration and signal-intensity normalization. Therefore, it is imperative to develop an ideal skull-stripping method in the field of brain image analysis. Previous reports have shown that convolutional neural network (CNN) method is better at skull stripping than non-CNN methods. We aimed to evaluate the accuracy of skull stripping in a single-contrast CNN model using eight-contrast magnetic resonance (MR) images. A total of 12 healthy participants and 12 patients with a clinical diagnosis of unilateral Sturge-Weber syndrome were included in our study. A 3-T MR imaging system and QRAPMASTER were used for data acquisition. We obtained eight-contrast images produced by post-processing T1, T2, and proton density (PD) maps. To evaluate the accuracy of skull stripping in our CNN method, gold-standard intracranial volume (ICVG) masks were used to train the CNN model. The ICVG masks were defined by experts using manual tracing. The accuracy of the intracranial volume obtained from the single-contrast CNN model (ICVE) was evaluated using the Dice similarity coefficient [= 2(ICVE ⋂ ICVG)/(ICVE + ICVG)]. Our study showed significantly higher accuracy in the PD-weighted image (WI), phase-sensitive inversion recovery (PSIR), and PD-short tau inversion recovery (STIR) compared to the other three contrast images (T1-WI, T2-fluid-attenuated inversion recovery [FLAIR], and T1-FLAIR). In conclusion, PD-WI, PSIR, and PD-STIR should be used instead of T1-WI for skull stripping in the CNN models.
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Affiliation(s)
- Masami Goto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
| | - Yujiro Otsuka
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Milliman Inc, Tokyo, Japan
- Plusman LLC, Tokyo, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hajime Sakamoto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yasuaki Sakano
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shinsuke Kyogoku
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Hiroyuki Daida
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
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Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy. Eur Radiol 2023; 33:2965-2974. [PMID: 36418622 DOI: 10.1007/s00330-022-09264-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/03/2022] [Accepted: 10/22/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..
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Samperna R, Moriakov N, Karssemeijer N, Teuwen J, Mann RM. Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI. Diagnostics (Basel) 2022; 12:diagnostics12071690. [PMID: 35885594 PMCID: PMC9324146 DOI: 10.3390/diagnostics12071690] [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] [Received: 05/30/2022] [Revised: 07/07/2022] [Accepted: 07/09/2022] [Indexed: 11/26/2022] Open
Abstract
Automatic breast and fibro-glandular tissue (FGT) segmentation in breast MRI allows for the efficient and accurate calculation of breast density. The U-Net architecture, either 2D or 3D, has already been shown to be effective at addressing the segmentation problem in breast MRI. However, the lack of publicly available datasets for this task has forced several authors to rely on internal datasets composed of either acquisitions without fat suppression (WOFS) or with fat suppression (FS), limiting the generalization of the approach. To solve this problem, we propose a data-centric approach, efficiently using the data available. By collecting a dataset of T1-weighted breast MRI acquisitions acquired with the use of the Dixon method, we train a network on both T1 WOFS and FS acquisitions while utilizing the same ground truth segmentation. Using the “plug-and-play” framework nnUNet, we achieve, on our internal test set, a Dice Similarity Coefficient (DSC) of 0.96 and 0.91 for WOFS breast and FGT segmentation and 0.95 and 0.86 for FS breast and FGT segmentation, respectively. On an external, publicly available dataset, a panel of breast radiologists rated the quality of our automatic segmentation with an average of 3.73 on a four-point scale, with an average percentage agreement of 67.5%.
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Affiliation(s)
- Riccardo Samperna
- Department of Medical Imaging, Radboudumc, 6525 GA Nijmegen, The Netherlands; (N.M.); (N.K.); (J.T.); (R.M.M.)
- Department of Radiology, The Netherlands Cancer Institute (NKI), 1066 CX Amsterdam, The Netherlands
- Correspondence:
| | - Nikita Moriakov
- Department of Medical Imaging, Radboudumc, 6525 GA Nijmegen, The Netherlands; (N.M.); (N.K.); (J.T.); (R.M.M.)
- Department of Radiation Oncology, The Netherlands Cancer Institute (NKI), 1066 CX Amsterdam, The Netherlands
| | - Nico Karssemeijer
- Department of Medical Imaging, Radboudumc, 6525 GA Nijmegen, The Netherlands; (N.M.); (N.K.); (J.T.); (R.M.M.)
- ScreenPoint Medical BV, 6525 EC Nijmegen, The Netherlands
| | - Jonas Teuwen
- Department of Medical Imaging, Radboudumc, 6525 GA Nijmegen, The Netherlands; (N.M.); (N.K.); (J.T.); (R.M.M.)
- Department of Radiation Oncology, The Netherlands Cancer Institute (NKI), 1066 CX Amsterdam, The Netherlands
| | - Ritse M. Mann
- Department of Medical Imaging, Radboudumc, 6525 GA Nijmegen, The Netherlands; (N.M.); (N.K.); (J.T.); (R.M.M.)
- Department of Radiology, The Netherlands Cancer Institute (NKI), 1066 CX Amsterdam, The Netherlands
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Yamamuro M, Asai Y, Hashimoto N, Yasuda N, Kimura H, Yamada T, Nemoto M, Kimura Y, Handa H, Yoshida H, Abe K, Tada M, Habe H, Nagaoka T, Nin S, Ishii K, Kondo Y. Utility of U-Net for the objective segmentation of the fibroglandular tissue region on clinical digital mammograms. Biomed Phys Eng Express 2022; 8. [PMID: 35728581 DOI: 10.1088/2057-1976/ac7ada] [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: 02/15/2022] [Accepted: 06/21/2022] [Indexed: 11/11/2022]
Abstract
This study investigates the equivalence or compatibility between U-Net and visual segmentations of fibroglandular tissue regions by mammography experts for calculating the breast density and mean glandular dose (MGD). A total of 703 mediolateral oblique-view mammograms were used for segmentation. Two region types were set as the ground truth (determined visually): (1) one type included only the region where fibroglandular tissue was identifiable (called the 'dense region'); (2) the other type included the region where the fibroglandular tissue may have existed in the past, provided that apparent adipose-only parts, such as the retromammary space, are excluded (the 'diffuse region'). U-Net was trained to segment the fibroglandular tissue region with an adaptive moment estimation optimiser, five-fold cross-validated with 400 training and 100 validation mammograms, and tested with 203 mammograms. The breast density and MGD were calculated using the van Engeland and Dance formulas, respectively, and compared between U-Net and the ground truth with the Dice similarity coefficient and Bland-Altman analysis. Dice similarity coefficients between U-Net and the ground truth were 0.895 and 0.939 for the dense and diffuse regions, respectively. In the Bland-Altman analysis, no proportional or fixed errors were discovered in either the dense or diffuse region for breast density, whereas a slight proportional error was discovered in both regions for the MGD (the slopes of the regression lines were -0.0299 and -0.0443 for the dense and diffuse regions, respectively). Consequently, the U-Net and ground truth were deemed equivalent (interchangeable) for breast density and compatible (interchangeable following four simple arithmetic operations) for MGD. U-Net-based segmentation of the fibroglandular tissue region was satisfactory for both regions, providing reliable segmentation for breast density and MGD calculations. U-Net will be useful in developing a reliable individualised screening-mammography programme, instead of relying on the visual judgement of mammography experts.
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Affiliation(s)
- Mika Yamamuro
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan.,Graduate School of Health Sciences, Niigata University, 2-746, Asahimachidori, Chuouku, Niigata 951-8518, Japan
| | - Yoshiyuki Asai
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Naomi Hashimoto
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Nao Yasuda
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Hiorto Kimura
- Radiology Center, Kindai University Hospital, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Takahiro Yamada
- Division of Positron Emission Tomography Institute of Advanced Clinical Medicine, Kindai University, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Mitsutaka Nemoto
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Yuichi Kimura
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Hisashi Handa
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Hisashi Yoshida
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Koji Abe
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Masahiro Tada
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Hitoshi Habe
- Department of Informatics, Kindai University Faculty of Science and Engineering, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan
| | - Takashi Nagaoka
- Department of Computational Systems Biology, Kindai University Faculty of Biology-Oriented Science and Technology, 930, Nishimitani, Kinokawa, Wakayama 649-6433, Japan
| | - Seiun Nin
- Department of Radiology, Kindai University Faculty of Medicine, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Kazunari Ishii
- Department of Radiology, Kindai University Faculty of Medicine, 377-2, Ono-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Yohan Kondo
- Graduate School of Health Sciences, Niigata University, 2-746, Asahimachidori, Chuouku, Niigata 951-8518, Japan
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Dey N, V. R. Magnetic resonance imaging: recording and reconstruction. Magn Reson Imaging 2022. [DOI: 10.1016/b978-0-12-823401-3.00003-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Chalfant JS, Mortazavi S, Lee-Felker SA. Background Parenchymal Enhancement on Breast MRI: Assessment and Clinical Implications. CURRENT RADIOLOGY REPORTS 2021. [DOI: 10.1007/s40134-021-00386-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Abstract
Purpose of Review
To present recent literature regarding the assessment and clinical implications of background parenchymal enhancement on breast MRI.
Recent Findings
The qualitative assessment of BPE remains variable within the literature, as well as in clinical practice. Several different quantitative approaches have been investigated in recent years, most commonly region of interest-based and segmentation-based assessments. However, quantitative assessment has not become standard in clinical practice to date. Numerous studies have demonstrated a clear association between higher BPE and future breast cancer risk. While higher BPE does not appear to significantly impact cancer detection, it may result in a higher abnormal interpretation rate. BPE is also likely a marker of pathologic complete response after neoadjuvant chemotherapy, with decreases in BPE during and after neoadjuvant chemotherapy correlated with pCR. In contrast, pre-treatment BPE does not appear to be predictive of pCR. The association between BPE and prognosis is less clear, with heterogeneous results in the literature.
Summary
Assessment of BPE continues to evolve, with heterogeneity in approaches to both qualitative and quantitative assessment. The level of BPE has important clinical implications, with associations with future breast cancer risk and treatment response. BPE may also be an imaging marker of prognosis, but future research is needed on this topic.
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Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model. J Digit Imaging 2021; 34:877-887. [PMID: 34244879 PMCID: PMC8455741 DOI: 10.1007/s10278-021-00472-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 05/27/2021] [Accepted: 06/09/2021] [Indexed: 12/11/2022] Open
Abstract
To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson’s correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.
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Hesse LS, Kuling G, Veta M, Martel AL. Intensity Augmentation to Improve Generalizability of Breast Segmentation Across Different MRI Scan Protocols. IEEE Trans Biomed Eng 2021; 68:759-770. [DOI: 10.1109/tbme.2020.3016602] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Meyer-Bäse A, Morra L, Meyer-Bäse U, Pinker K. Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2020; 2020:6805710. [PMID: 32934610 PMCID: PMC7474774 DOI: 10.1155/2020/6805710] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/17/2020] [Accepted: 05/28/2020] [Indexed: 12/12/2022]
Abstract
Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.
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Affiliation(s)
- Anke Meyer-Bäse
- Department of Scientific Computing, Florida State University, Tallahassee, Florida 32310-4120, USA
| | - Lia Morra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy
| | - Uwe Meyer-Bäse
- Department of Electrical and Computer Engineering, Florida A&M University and Florida State University, Tallahassee, Florida 32310-4120, USA
| | - Katja Pinker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA
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Liu M, Vanguri R, Mutasa S, Ha R, Liu YC, Button T, Jambawalikar S. Channel width optimized neural networks for liver and vessel segmentation in liver iron quantification. Comput Biol Med 2020; 122:103798. [PMID: 32658724 DOI: 10.1016/j.compbiomed.2020.103798] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/27/2020] [Accepted: 04/29/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION MRI T2* relaxometry protocols are often used for Liver Iron Quantification in patients with hemochromatosis. Several methods exist to semi-automatically segment parenchyma and exclude vessels for this calculation. PURPOSE To determine if inclusion of multiple echoes inputs to Convolutional Neural Networks (CNN) improves automated liver and vessel segmentation in MRI T2* relaxometry protocols and to determine if the resultant segmentations agree with manual segmentations for liver iron quantification analysis. METHODS Multi echo Gradient Recalled Echo (GRE) MRI sequence for T2* relaxometry was performed for 79 exams on 31 patients with hemochromatosis for iron quantification analysis. 275 axial liver slices were manually segmented as ground truth masks. A batch normalized U-Net with variable input width to incorporate multiple echoes is used for segmentation, using DICE as the accuracy metric. ANOVA is used to evaluate significance of channel width changes in segmentation accuracy. Linear regression is used to model the relationship of channel width on segmentation accuracy. Liver segmentations are applied to relaxometry data to calculate liver T2* yielding liver iron concentration(LIC) derived from literature based calibration curves. Manual and CNN based LIC values are compared with Pearson correlation. Bland altman plots are used to visualize differences between manual and CNN based LIC values. RESULTS Performance metrics are tested on 55 hold out slices. Linear regression indicates that there is a monotonic increase of DICE with increasing channel depth (p = 0.001) with a slope of 3.61e-3. ANOVA indicates a significant increase segmentation accuracy over single channel starting at 3 channels. Incorporation of all channels results in an average DICE of 0.86, an average increase of 0.07 over single channel. The calculated LIC from CNN segmented livers agrees well with manual segmentation (R = 0.998, slope = 0.914, p«0.001), with an average absolute difference 0.27 ± 0.99 mg Fe/g or 1.34 ± 4.3%. CONCLUSION More input echoes yields higher model accuracy until the noise floor. Echos beyond the first three echo times in GRE based T2* relaxometry do not contribute significant information for segmentation of liver for LIC calculation. Deep learning models with three channel width allow for generalization of model to protocols of more than three echoes, effectively a universal requirement for relaxometry. Deep learning segmentations achieve a good accuracy compared with manual segmentations with minimal preprocessing. Liver iron values calculated from hand segmented liver and Neural network segmented liver were not statistically different from each other.
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Affiliation(s)
- Michael Liu
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA.
| | - Rami Vanguri
- Department of Pathology & Cell Biology, Columbia University, New York, NY, USA
| | - Simukayi Mutasa
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA
| | - Richard Ha
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA
| | - Yu-Cheng Liu
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA
| | - Terry Button
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY, 10032, USA
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Chhetri A, Li X, Rispoli JV. Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer. Front Med (Lausanne) 2020; 7:175. [PMID: 32478083 PMCID: PMC7235971 DOI: 10.3389/fmed.2020.00175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 04/15/2020] [Indexed: 01/10/2023] Open
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide, and early detection remains a principal factor for improved patient outcomes and reduced mortality. Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. Static MRI techniques enable superior structural contrast between adipose and fibroglandular tissues, while dynamic MRI techniques can elucidate functional characteristics of malignant tumors. The preferred clinical procedure-dynamic contrast-enhanced MRI-illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic contrast agent remains a major limitation of the technique, propelling MRI research toward finding an alternative, noninvasive method. Three such techniques are magnetic resonance spectroscopy, chemical exchange saturation transfer, and non-contrast diffusion weighted imaging. These methods shed light on underlying chemical composition, provide snapshots of tissue metabolism, and more pronouncedly characterize microstructural heterogeneity. This review article outlines the present state of clinical MRI for breast cancer and examines several research techniques that demonstrate capacity for clinical translation. Ultimately, multi-parametric MRI-incorporating one or more of these emerging methods-presently holds the best potential to afford improved specificity and deliver excellent accuracy to clinics for the prediction, detection, and monitoring of breast cancer.
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Affiliation(s)
- Apekshya Chhetri
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Xin Li
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Joseph V. Rispoli
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Center for Cancer Research, Purdue University, West Lafayette, IN, United States
- School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, United States
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Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging 2019; 52:998-1018. [PMID: 31276247 DOI: 10.1002/jmri.26852] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 12/13/2022] Open
Abstract
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.
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Affiliation(s)
- Beatriu Reig
- The Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Krzysztof J Geras
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
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Babarenda Gamage TP, Malcolm DTK, Maso Talou G, Mîra A, Doyle A, Nielsen PMF, Nash MP. An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment. Interface Focus 2019; 9:20190034. [PMID: 31263540 DOI: 10.1098/rsfs.2019.0034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2019] [Indexed: 12/24/2022] Open
Abstract
Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detection and treatment procedures. This paper summarizes our recent research to address the various technical and implementation challenges associated with creating a fully automated system. The workflow is applied to predict the repositioning of tumours from the prone position, where diagnostic magnetic resonance imaging is performed, to the supine position where treatment procedures are performed. We discuss our recent advances towards addressing challenges in identifying the mechanical properties of the breast and evaluating the accuracy of the biomechanical models. We also describe our progress in implementing a prototype of this workflow in clinical practice. Clinical adoption of these state-of-the-art modelling techniques has significant potential for reducing the number of misdiagnosed breast cancers, while also helping to improve the treatment of patients.
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Affiliation(s)
| | - Duane T K Malcolm
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Gonzalo Maso Talou
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Anna Mîra
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Anthony Doyle
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Poul M F Nielsen
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.,Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.,Department of Engineering Science, University of Auckland, Auckland, New Zealand
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