Automated segmentation of the injured kidney due to abdominal trauma
| Autor: | Ozgur Dandin, Murathan Koksal, Ferhat Cuce, Gokalp Tulum, Uygar Teomete, Onur Osman, Tuncer Ergin |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
020205 medical informatics Solid Organ Injuries Automated segmentation Medicine (miscellaneous) Health Informatics Kidney Volume Abdominal Injuries 02 engineering and technology Automated Segmentation Injured Kidney Automation Imaging Three-Dimensional Hematoma Health Information Management Sørensen–Dice coefficient 0202 electrical engineering electronic engineering information engineering Humans Medicine Segmentation Kidney business.industry Acute Kidney Injury medicine.disease Extravasation Abdominal Trauma medicine.anatomical_structure Abdominal trauma Radiographic Image Interpretation Computer-Assisted Female Tomography X-Ray Computed business Nuclear medicine Algorithms Information Systems |
| Popis: | Tulum, Gökalp (Arel Author) Osman, Onur (Arel Author) The objective of this study is to propose and validate a computer-aided segmentation system which performs the automated segmentation of injured kidney in the presence of contusion, peri-, intra-, sub-capsular hematoma, laceration, active extravasation and urine leak due to abdominal trauma. In the present study, total multi-phase CT scans of thirty-seven cases were used; seventeen of them for the development of the method and twenty of them for the validation of the method. The proposed algorithm contains three steps: determination of the kidney mask using Circular Hough Transform, segmentation of the renal parenchyma of the kidney applying the symmetry property to the histogram, and estimation of the kidney volume. The results of the proposed method were compared using various metrics. The kidney quantification led to 92.3 +/- 4.2% Dice coefficient, 92.8 +/- 7.4%/92.3 +/- 5.1% precision/sensitivity, 1.4 +/- 0.6 mm/2.0 +/- 1.0 mm average surface distance/root-mean-squared error for intact and 87.3 +/- 8.4% Dice coefficient, 84.3 +/- 13.8%/92.2 +/- 3.8% precision/sensitivity and 2.4 +/- 2.2 mm/4.0 +/- 4.2 mm average surface distance/root-mean-squared error for injured kidneys. The segmentation of the injured kidney was satisfactorily performed in all cases. This method may lead to the automated detection of renal lesions due to abdominal trauma and estimate the intraperitoneal blood amount, which is vital for trauma patients. |
| Databáze: | OpenAIRE |
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