{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T04:11:22Z","timestamp":1769314282305,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.<\/jats:p>","DOI":"10.3390\/e23040382","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T15:42:19Z","timestamp":1616600539000},"page":"382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6219-8671","authenticated-orcid":false,"given":"Julie","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Alexander","family":"Wood","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Chao","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Kayvan","family":"Najarian","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5125-7741","authenticated-orcid":false,"given":"Jonathan","family":"Gryak","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/j.crad.2019.07.017","article-title":"CT of blunt splenic injuries: What the trauma team wants to know from the radiologist","volume":"74","author":"Shi","year":"2019","journal-title":"Clin. Radiol."},{"key":"ref_2","first-page":"60","article-title":"Computed tomography of blunt spleen injury: A pictorial review","volume":"18","author":"Hassan","year":"2011","journal-title":"Malays. J. Med. Sci. MJMS"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.compbiomed.2019.02.017","article-title":"Radiological images and machine learning: Trends, perspectives, and prospects","volume":"108","author":"Zhang","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","article-title":"Computer-aided diagnosis in medical imaging: Historical review, current status and future potential","volume":"31","author":"Doi","year":"2007","journal-title":"Comput. Med Imaging Graph."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.jacr.2018.01.028","article-title":"Role of big data and machine learning in diagnostic decision support in radiology","volume":"15","year":"2018","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wood, A., Soroushmehr, S.R., Farzaneh, N., Fessell, D., Ward, K.R., Gryak, J., Kahrobaei, D., and Najarian, K. (2018, January 18\u201321). Fully Automated Spleen Localization In addition, Segmentation Using Machine Learning In addition, 3D Active Contours. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512182"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1016\/j.dsp.2009.10.010","article-title":"Detection and classification of masses in breast ultrasound images","volume":"20","author":"Shi","year":"2010","journal-title":"Digit. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1002\/ima.22052","article-title":"An intelligent mining system for diagnosing medical images using combined texture-histogram features","volume":"23","author":"Dhanalakshmi","year":"2013","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TMI.2003.809593","article-title":"Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform","volume":"22","author":"Lee","year":"2003","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, Y., Lin, L., Hu, H., Yu, H., Jin, C., Wang, J., Han, X., and Chen, Y.W. (2016). Combined density, texture and shape features of multi-phase contrast-enhanced CT images for CBIR of focal liver lesions: A preliminary study. Innovation in Medicine and Healthcare 2015, Springer.","DOI":"10.1007\/978-3-319-23024-5_20"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1007\/s10278-015-9857-6","article-title":"A combination of shape and texture features for classification of pulmonary nodules in lung CT images","volume":"29","author":"Dhara","year":"2016","journal-title":"J. Digit. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s12938-016-0146-5","article-title":"A method of localization and segmentation of intervertebral discs in spine MRI based on Gabor filter bank","volume":"15","author":"Zhu","year":"2016","journal-title":"Biomed. Eng. Online"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9389","DOI":"10.1016\/j.eswa.2012.02.128","article-title":"Ultrasonic liver tissue characterization by feature fusion","volume":"39","author":"Wu","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3683","DOI":"10.1016\/j.asoc.2013.03.009","article-title":"An ensemble-based data fusion approach for characterizing ultrasonic liver tissue","volume":"13","author":"Lee","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_15","first-page":"212","article-title":"Content-based image retrieval using local features descriptors and bag-of-visual words","volume":"6","author":"Alkhawlani","year":"2015","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_16","unstructured":"U.S. Department of Transportation, National Highway Traffic Safety Administration (NHTSA) (2021, February 01). Crash Injury Research Engineering Network, Available online: https:\/\/www.nhtsa.gov\/research-data\/crash-injury-research."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/TPAMI.1987.4767956","article-title":"Characteristics of natural scenes related to the fractal dimension","volume":"9","author":"Keller","year":"1987","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.camwa.2013.01.017","article-title":"Entropy of fractal systems","volume":"66","author":"Zmeskal","year":"2013","journal-title":"Comput. Math. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sergyan, S. (2008, January 21\u201322). Color histogram features based image classification in content-based image retrieval systems. Proceedings of the 2008 6th International Symposium on Applied Machine Intelligence and Informatics, Herlany, Slovakia.","DOI":"10.1109\/SAMI.2008.4469170"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mandelbrot, B.B. (1983). The Fractal Geometry of Nature, WH freeman.","DOI":"10.1119\/1.13295"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/42.24861","article-title":"Fractal feature analysis and classification in medical imaging","volume":"8","author":"Chen","year":"1989","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_22","unstructured":"Zheng, D., Zhao, Y., and Wang, J. (2004, January 23\u201325). Features extraction using a Gabor filter family. Proceedings of the Sixth IASTED International Conference, Signal and Image Processing, Honolulu, HI, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7905","DOI":"10.1016\/j.eswa.2015.06.025","article-title":"CloudID: Trustworthy cloud-based and cross-enterprise biometric identification","volume":"42","author":"Haghighat","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s13755-018-0059-8","article-title":"Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images","volume":"6","author":"Ashour","year":"2018","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep learning in medical imaging: General overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Burduja, M., Ionescu, R.T., and Verga, N. (2020). Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks. Sensors, 20.","DOI":"10.3390\/s20195611"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nguyen, N.T., Tran, D.Q., Nguyen, N.T., and Nguyen, H.Q. (2020). A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans. arXiv.","DOI":"10.1101\/2020.04.17.20070193"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11517-020-02302-w","article-title":"Lung cancer histology classification from CT images based on radiomics and deep learning models","volume":"59","author":"Marentakis","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kutlu, H., and Avc\u0131, E. (2019). A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors, 19.","DOI":"10.3390\/s19091992"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Luo, C., Li, X., Wang, L., He, J., Li, D., and Zhou, J. (2018, January 10\u201312). How Does the Data set Affect CNN-based Image Classification Performance?. Proceedings of the 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China.","DOI":"10.1109\/ICSAI.2018.8599448"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-48738-5","article-title":"Image-based classification of tumor type and growth rate using machine learning: A preclinical study","volume":"9","author":"Tang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1540025","DOI":"10.1142\/S0219519415400254","article-title":"Random forest based classification of medical x-ray images using a genetic algorithm for feature selection","volume":"15","author":"Nedjar","year":"2015","journal-title":"J. Mech. Med. Biol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.neuroimage.2011.03.080","article-title":"Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images","volume":"57","author":"Geremia","year":"2011","journal-title":"NeuroImage"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.nicl.2014.08.023","article-title":"Random Forest ensembles for detection and prediction of Alzheimer\u2019s disease with a good between-cohort robustness","volume":"6","author":"Lebedev","year":"2014","journal-title":"Neuroimage Clin."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"012005","DOI":"10.1088\/1742-6596\/851\/1\/012005","article-title":"Effect of slice thickness on image noise and diagnostic content of single-source-dual energy computed tomography","volume":"851","author":"Alshipli","year":"2017","journal-title":"J. Phys. Conf. Ser. IOP Publ."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/4\/382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:40:05Z","timestamp":1760161205000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/4\/382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,24]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["e23040382"],"URL":"https:\/\/doi.org\/10.3390\/e23040382","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,24]]}}}