journal article·2022-07-26Open access

Image Processing and Analysis for Preclinical and Clinical Applications

Alessandro Stefano, Federica Vernuccio, Albert Comelli

Applied Sciences· Volume 12· Issue 15

Abstract

Preclinical and clinical imaging aims to characterize and measure biological processes and diseases in animals [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short

Research context

OpenAlex topics: Digital Imaging for Blood Diseases · Cell Image Analysis Techniques · Infrared Thermography in Medicine

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy

Department of Radiology, University Hospital of Padova, Via Nicolò Giustiniani 2, 35128 Padova, Italy

Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy

References (23)

Show source-provided references
1. Benfante, V., Stefano, A., Comelli, A., Giaccone, P., Cammarata, F.P., Richiusa, S., Scopelliti, F., Pometti, M., Ficarra, M., and Cosentino, S. (2022). A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mouse Models. J. Imaging, 8.10.3390/jimaging8040092 ↗
2. Stefano, A., and Comelli, A. (2021). Customized efficient neural network for covid-19 infected region identification in ct images. J. Imaging, 7.10.3390/jimaging7080131 ↗
3. Predictive and Prognostic Value of Early Disease Progression by PET Evaluation in Advanced Non-Small Cell Lung Cancer10.1159/000448005 ↗
4. Stefano, A., Gioè, M., Russo, G., Palmucci, S., Torrisi, S.E., Bignardi, S., Basile, A., Comelli, A., Benfante, V., and Sambataro, G. (2020). Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT. Diagnostics, 10.10.3390/diagnostics10050306 ↗
5. Cutaia, G., la Tona, G., Comelli, A., Vernuccio, F., Agnello, F., Gagliardo, C., Salvaggio, L., Quartuccio, N., Sturiale, L., and Stefano, A. (2021). Radiomics and prostate MRI: Current role and future applications. J. Imaging, 7.10.3390/jimaging7020034 ↗
6. Albayrak, N.B., and Akgul, Y.S. (2022). Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting. Appl. Sci., 12.10.3390/app12031390 ↗
7. Comelli, A., Dahiya, N., Stefano, A., Vernuccio, F., Portoghese, M., Cutaia, G., Bruno, A., Salvaggio, G., and Yezzi, A. (2021). Deep learning-based methods for prostate segmentation in magnetic resonance imaging. Appl. Sci., 11.10.3390/app11020782 ↗
8. Salvaggio, G., Cutaia, G., Greco, A., Pace, M., Salvaggio, L., Vernuccio, F., Cannella, R., Algeri, L., Incorvaia, L., and Stefano, A. (2022). Deep Learning Networks for Automatic Retroperitoneal Sarcoma Segmentation in Computerized Tomography. Appl. Sci., 12.10.3390/app12031665 ↗
9. Vélez, P., Miranda, M., Serrano, C., and Acha, B. (2022). Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?. Appl. Sci., 12.10.3390/app12042092 ↗
10. Amodeo, M., Abbate, V., Arpaia, P., Cuocolo, R., Orabona, G.D., Murero, M., Parvis, M., Prevete, R., and Ugga, L. (2021). Transfer learning for an automated detection system of fractures in patients with maxillofacial trauma. Appl. Sci., 11.10.3390/app11146293 ↗
11. Deep learning approach for the segmentation of aneurysmal ascending aorta10.1007/s13534-020-00179-0 ↗
12. Alongi, P., Stefano, A., Comelli, A., Spataro, A., Formica, G., Laudicella, R., Lanzafame, H., Panasiti, F., Longo, C., and Midiri, F. (2022). Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome. Appl. Sci., 12.10.3390/app12062941 ↗
13. Stefano, A., Leal, A., Richiusa, S., Trang, P., Comelli, A., Benfante, V., Cosentino, S., Sabini, M.G., Tuttolomondo, A., and Altieri, R. (2021). Robustness of pet radiomics features: Impact of co-registration with mri. Appl. Sci., 11.10.3390/app112110170 ↗
14. Lee, S., Lim, J., Shin, J., Kim, S., and Hwang, H. (2021). Pathologic Complete Response Prediction after Neoadjuvant Chemoradiation Therapy for Rectal Cancer Using Radiomics and Deep Embedding Network of MRI. Appl. Sci., 11.10.3390/app11209494 ↗
15. Hybrid descriptive-inferential method for key feature selection in prostate cancer radiomics10.1002/asmb.2642 ↗
16. Stefano, A., Comelli, A., Bravatà, V., Barone, S., Daskalovski, I., Savoca, G., Sabini, M.G., Ippolito, M., and Russo, G. (2020). A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method. BMC Bioinform., 21.10.1186/s12859-020-03647-7 ↗
17. Stefano, A., Pisciotta, P., Pometti, M., Comelli, A., Cosentino, S., Marletta, F., Cicero, S., Sabini, M.G., Ippolito, M., and Russo, G. (2021). Early monitoring response to therapy in patients with brain lesions using the cumulative SUV histogram. Appl. Sci., 11.10.3390/app11072999 ↗
18. Carreira, M.J., Vila-Blanco, N., Cabezas-Sainz, P., and Sánchez, L. (2021). Zftool: A software for automatic quantification of cancer cell mass evolution in zebrafish. Appl. Sci., 11.10.20944/preprints202107.0695.v1 ↗
19. Hsu, W.L., Chang, S.M., and Chang, C.C. (2022). Clinical Comparison of the Glomerular Filtration Rate Calculated from Different Renal Depths and Formulae. Appl. Sci., 12.10.3390/app12020698 ↗
20. Sheitt, H., Kim, H., Wilton, S., White, J.A., and Garcia, J. (2021). Left atrial flow stasis in patients undergoing pulmonary vein isolation for paroxysmal atrial fibrillation using 4d-flow magnetic resonance imaging. Appl. Sci., 11.10.3390/app11125432 ↗
21. An automatic method for metabolic evaluation of gamma knife treatments
22. Fundus image registration technique based on local feature of retinal vessels10.3390/app112311201 ↗
23. Hardware Optimizations of the X-ray Pre-Processing for Interventional Computed Tomography Using the FPGA10.3390/app12115659 ↗