9 research outputs found
Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks
[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolutionThis work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks. Applied Sciences. 11(2):1-17. https://doi.org/10.3390/app11020844S117112Marra, G., Ploussard, G., Futterer, J., & Valerio, M. (2019). Controversies in MR targeted biopsy: alone or combined, cognitive versus software-based fusion, transrectal versus transperineal approach? World Journal of Urology, 37(2), 277-287. doi:10.1007/s00345-018-02622-5Ahdoot, M., Lebastchi, A. H., Turkbey, B., Wood, B., & Pinto, P. A. (2019). Contemporary treatments in prostate cancer focal therapy. Current Opinion in Oncology, 31(3), 200-206. doi:10.1097/cco.0000000000000515Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Allen, P. D., Graham, J., Williamson, D. C., & Hutchinson, C. E. (s. f.). Differential Segmentation of the Prostate in MR Images Using Combined 3D Shape Modelling and Voxel Classification. 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006. doi:10.1109/isbi.2006.1624940Freedman, D., Radke, R. J., Tao Zhang, Yongwon Jeong, Lovelock, D. M., & Chen, G. T. Y. (2005). Model-based segmentation of medical imagery by matching distributions. IEEE Transactions on Medical Imaging, 24(3), 281-292. doi:10.1109/tmi.2004.841228Klein, S., van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., & Pluim, J. P. W. (2008). Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics, 35(4), 1407-1417. doi:10.1118/1.2842076Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234-241. doi:10.1007/978-3-319-24574-4_28He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2017.322Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. doi:10.1109/tpami.2016.2572683He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2016.90Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV). doi:10.1109/3dv.2016.79Zhu, Q., Du, B., Turkbey, B., Choyke, P. L., & Yan, P. (2017). Deeply-supervised CNN for prostate segmentation. 2017 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2017.7965852To, M. N. N., Vu, D. Q., Turkbey, B., Choyke, P. L., & Kwak, J. T. (2018). Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery, 13(11), 1687-1696. doi:10.1007/s11548-018-1841-4Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.243Zhu, Y., Wei, R., Gao, G., Ding, L., Zhang, X., Wang, X., & Zhang, J. (2018). Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. Journal of Magnetic Resonance Imaging, 49(4), 1149-1156. doi:10.1002/jmri.26337Wang, Y., Ni, D., Dou, H., Hu, X., Zhu, L., Yang, X., … Wang, T. (2019). Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound. IEEE Transactions on Medical Imaging, 38(12), 2768-2778. doi:10.1109/tmi.2019.2913184Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., Walker, P. M., & Meriaudeau, F. (2015). Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine, 60, 8-31. doi:10.1016/j.compbiomed.2015.02.009Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359-373. doi:10.1016/j.media.2013.12.002Zhu, Q., Du, B., & Yan, P. (2020). Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation. IEEE Transactions on Medical Imaging, 39(3), 753-763. doi:10.1109/tmi.2019.2935018He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.123Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. doi:10.1109/tkde.2009.191Smith, L. N. (2017). Cyclical Learning Rates for Training Neural Networks. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2017.58Abraham, N., & Khan, N. M. (2019). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759329Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P., … Yang, X. (2019). Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net. Medical Physics, 46(7), 3194-3206. doi:10.1002/mp.13577Orlando, N., Gillies, D. J., Gyacskov, I., Romagnoli, C., D’Souza, D., & Fenster, A. (2020). Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images. Medical Physics, 47(6), 2413-2426. doi:10.1002/mp.14134Karimi, D., Zeng, Q., Mathur, P., Avinash, A., Mahdavi, S., Spadinger, I., … Salcudean, S. E. (2019). Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Medical Image Analysis, 57, 186-196. doi:10.1016/j.media.2019.07.005PROMISE12 Resultshttps://promise12.grand-challenge.org/Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203-211. doi:10.1038/s41592-020-01008-
New GOLD classification: longitudinal data on group assignment
Rationale: Little is known about the longitudinal changes associated with using the 2013 update of the
multidimensional GOLD strategy for chronic obstructive pulmonary disease (COPD).
Objective: To determine the COPD patient distribution of the new GOLD proposal and evaluate how this
classification changes over one year compared with the previous GOLD staging based on spirometry only.
Methods: We analyzed data from the CHAIN study, a multicenter observational Spanish cohort of COPD patients
who are monitored annually. Categories were defined according to the proposed GOLD: FEV1%, mMRC dyspnea,
COPD Assessment Test (CAT), Clinical COPD Questionnaire (CCQ), and exacerbations-hospitalizations. One-year
follow-up information was available for all variables except CCQ data.
Results: At baseline, 828 stable COPD patients were evaluated. On the basis of mMRC dyspnea versus CAT, the
patients were distributed as follows: 38.2% vs. 27.2% in group A, 17.6% vs. 28.3% in group B, 15.8% vs. 12.9% in
group C, and 28.4% vs. 31.6% in group D. Information was available for 526 patients at one year: 64.2% of patients
remained in the same group but groups C and D show different degrees of variability. The annual progression by
group was mainly associated with one-year changes in CAT scores (RR, 1.138; 95%CI: 1.074-1.206) and BODE index
values (RR, 2.012; 95%CI: 1.487-2.722).
Conclusions: In the new GOLD grading classification, the type of tool used to determine the level of symptoms
can substantially alter the group assignment. A change in category after one year was associated with longitudinal
changes in the CAT and BODE index
New GOLD classification: longitudinal data on group assignment
In the new GOLD grading classification, the type of tool used to determine the level of symptoms can substantially alter the group assignment. A change in category after one year was associated with longitudinal changes in the CAT and BODE index
Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)
Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters.
Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs).
Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001).
Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio
Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images
Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), analyzing these images remains still complex even for experts. This paper proposes a fully automatic system based on Deep Learning that performs localization, segmentation and Gleason grade group (GGG) estimation of PCa lesions from prostate mpMRIs. It uses 490 mpMRIs for training/validation and 75 for testing from two different datasets: ProstateX and Valencian Oncology Institute Foundation. In the test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for the GGG 2 significance criterion of 0.96/1.00/0.79 for the ProstateX dataset, and 0.95/1.00/0.80 for the IVO dataset. At a patient level, the results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO. Furthermore, on the online ProstateX grand challenge, the model obtained an AUC of 0.85 (0.87 when trained only on the ProstateX data, tying up with the original winner of the challenge). For expert comparison, IVO radiologist's PI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and 0.85/0.58 at a patient level. The full code for the ProstateX-trained model is openly available at https://github.com/OscarPellicer/prostate_lesion_detection. We hope that this will represent a landmark for future research to use, compare and improve upon
Clinical Application of the COPD Assessment Test: Longitudinal Data From the COPD History Assessment in Spain (CHAIN) Cohort
OBJECTIVE: The COPD Assessment Test (CAT) has been proposed for assessing health status in COPD, but little is known about its longitudinal changes. The objective of this study was to evaluate 1-year CAT variability in patients with stable COPD and to relate its variations to changes in other disease markers. METHODS: We evaluated the following variables in smokers with and without COPD at baseline and aft er 1 year: CAT score, age, sex, smoking status, pack-year history, BMI, modified Medical Research Council (mMRC) scale, 6-min walk distance (6MWD), lung function, BODE (BMI, obstruction, dyspnea, exercise capacity) index, hospital admissions, Hospital and Depression Scale, and the Charlson comorbidity index. In patients with COPD, we explored the association of CAT scores and 1-year changes in the studied parameters. R ESULTS: A total of 824 smokers with COPD and 126 without COPD were evaluated at baseline and 441 smokers with COPD and 66 without COPD 1 year later. At 1 year, CAT scores for patients with COPD were similar ( ± 4 points) in 56%, higher in 27%, and lower in 17%. Of note, mMRC scale scores were similar ( ± 1 point) in 46% of patients, worse in 36%, and better in 18% at 1 year. One-year CAT changes were best predicted by changes in mMRC scale scores ( β -coefficient, 0.47; P<, .001). Similar results were found for CAT and mMRC scale score in smokers without COPD. CONCLUSIONS: One-year longitudinal data show variability in CAT scores among patients with stable COPD similar to mMRC scale score, which is the best predictor of 1-year CAT changes. Further longitudinal studies should confirm long-term CAT variability and its clinical applicability. © 2014 AMERICAN COLLEGE OF CHEST PHYSICIANS.The
authors have reported to CHEST the
follow ing conflicts of interest: Dr de Torres
received fees for speaking activities for
GlaxoSmithKline plc, AstraZeneca,
Novartis AG, Merck Sharp & Dohme Corp,
and Takeda Pharmaceuticals International
GmbH and received consultancy fees for
participating on advisory boards for Takeda
Pharmaceuticals International GmbH
and Novartis AG between 2010 and 2013.
Dr Martinez-Gonzalez received fees for
speaking activities for Almirall, SA;
AstraZeneca; Boehringer Ingelheim GmbH;
Pfi zer Inc; GlaxoSmithKline plc; and Chiesi
Farmaceutici SpA between 2010 and
2013. Dr de Lucas-Ramos received fees for
speaking activities for Almirall, SA; Boehringer
Ingelheim GmbH; Takeda Pharmaceuticals
International GmbH; and GlaxoSmithKline
plc and received grants from Almirall, SA,
and Foundation Vital Aire between 2010
and 2013. Dr Cosio received fees for
speaking activities for Almirall, SA; Takeda
Pharmaceuticals International GmbH; The
Menarini Group; Boehringer Ingelheim
GmbH; Pfizer Inc; GlaxoSmithKline plc;
and Chiesi Farmaceutici SpA between
2010 and 2013. Dr Peces-Barba received
fees for speaking activities for Almirall,
SA; Takeda Pharmaceuticals International
GmbH; Novartis AG; Boehringer Ingelheim
GmbH; AstraZeneca; Esteve; GlaxoSmithKline
plc, and Chiesi Farmaceutici SpA; received
consultancy fees for participating in advisory
boards of Takeda Pharmaceuticals
International GmbH, Novartis AG, and
Ferrer Internacional; and received grants
from GlaxoSmithKline plc between 2010
and 2013. Dr Solanes-García received fees
for speaking activities for Esteve; AstraZeneca;
Th e Menarini Group; Boehringer Ingelheim
GmbH; Pfizer Inc; GlaxoSmithKline plc,
Biodatos Investigación SL, and Chiesi
Farmaceutici SpA between 2010 and 2013.
Dr Agüero Balbin received fees for speaking
activities for Almirall, SA; AstraZeneca;
Novartis AG; Boehringer Ingelheim
GmbH; Takeda Pharmaceuticals International
GmbH; GlaxoSmithKline plc; and
Chiesi Farmaceutici SpA between 2010
and 2013. Dr de Diego-Damia received
fees for speaking activities for Boehringer
Ingelheim GmbH, AstraZeneca, Pfizer Inc,
Merck Sharp & Dohme Corp, GlaxoSmithKline
plc, and Chiesi Farmaceutici SpA between
2010 and 2013. Dr Alfageme Michavila
received fees for speaking activities for
Almirall, SA; Boehringer Ingelheim
GmbH; and Pfizer Inc between 2010 and
2013. Dr Irigaray received fees for speaking
activities for Novartis AG, Takeda
Pharmaceuticals International GmbH,
GlaxoSmithKline plc, and Chiesi Farmaceutici
SpA between 2010 and 2013. Dr Llunell
Casanovas received fees for speaking activities
for AstraZeneca, Eli Lilly and Co, and
Chiesi Farmaceutici SpA between 2010
and 2013. Dr Galdiz Iturri received fees for
speaking activities for Almirall, SA; Novartis
AG; AstraZeneca; Boehringer Ingelheim
GmbH; GlaxoSmithKline plc; and Chiesi
Farmaceutici SpA between 2010 and 2013.
Dr Soler-Cataluña participated in speaking
activities, on an industry advisory committee,
or with other related activities sponsored
by Almirall, SA; AstraZeneca; Boehringer
Ingelheim GmbH; Pfizer Inc; Ferrer
Internacional; GlaxoSmithKline plc; Takeda
Pharmaceuticals International GmbH;
Merck Sharp & Dohme Corp; Novartis
AG; and Grupo Uriach between 2010 and
2013. Dr Soriano received grants from
GlaxoSmithKline plc in 2011 and Chiesi
Farmaceutici SpA in 2012 through his
home institution and participated in
speaking activities, on an industry advisory
committee, or with other related
activities sponsored by Almirall, SA;
Boehringer Ingelheim GmbH; Pfizer Inc;
Chiesi Farmaceutici SpA; GlaxoSmithKline
plc; and Novartis AG between 2010 and
2013. Dr Casanova participated in speaking
activities for Almirall, SA; Takeda
Pharmaceuticals International GmbH;
Chiesi Farmaceutici SpA; GlaxoSmithKline
plc; and Novartis AG between 2010 and
2013. Drs Marin, Mir-Viladrich, CalleRubio,
Feu-Collado, Balcells, Marín Royo,
and Lopez-Campos have reported that no
potential conflicts of interest exist with any
companies/organizations whose products
or services may be discussed in this article
Prevalence of persistent blood eosinophilia: relation to outcomes in patients with COPD.
The impact of blood eosinophilia in chronic obstructive pulmonary disease (COPD) remains controversial.To evaluate the prevalence and stability of a high level of blood eosinophils (≥300 cells·μL-1) and its relationship to outcomes, we determined blood eosinophils at baseline and over 2 years in 424 COPD patients (forced expiratory volume in 1 s (FEV1) 60% predicted) and 67 smokers without COPD from the CHAIN cohort, and in 308 COPD patients (FEV1 60% predicted) in the BODE cohort. We related eosinophil levels to exacerbations and survival using Cox hazard analysis.In COPD patients, 15.8% in the CHAIN cohort and 12.3% in the BODE cohort had persistently elevated blood eosinophils at all three visits. A significant proportion (43.8%) of patients had counts that oscillated above and below the cut-off points, while the rest had persistent eosinophil level