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Farzaneh N, Stein EB, Soroushmehr R, Gryak J, Najarian K. A deep learning framework for automated detection and quantitative assessment of liver trauma. BMC Med Imaging 2022; 22:39. [PMID: 35260105 PMCID: PMC8905785 DOI: 10.1186/s12880-022-00759-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 02/17/2022] [Indexed: 11/19/2022] Open
Abstract
Background Both early detection and severity assessment of liver trauma are critical for optimal triage and management of trauma patients. Current trauma protocols utilize computed tomography (CT) assessment of injuries in a subjective and qualitative (v.s. quantitative) fashion, shortcomings which could both be addressed by automated computer-aided systems that are capable of generating real-time reproducible and quantitative information. This study outlines an end-to-end pipeline to calculate the percentage of the liver parenchyma disrupted by trauma, an important component of the American Association for the Surgery of Trauma (AAST) liver injury scale, the primary tool to assess liver trauma severity at CT. Methods This framework comprises deep convolutional neural networks that first generate initial masks of both liver parenchyma (including normal and affected liver) and regions affected by trauma using three dimensional contrast-enhanced CT scans. Next, during the post-processing step, human domain knowledge about the location and intensity distribution of liver trauma is integrated into the model to avoid false positive regions. After generating the liver parenchyma and trauma masks, the corresponding volumes are calculated. Liver parenchymal disruption is then computed as the volume of the liver parenchyma that is disrupted by trauma. Results The proposed model was trained and validated on an internal dataset from the University of Michigan Health System (UMHS) including 77 CT scans (34 with and 43 without liver parenchymal trauma). The Dice/recall/precision coefficients of the proposed segmentation models are 96.13/96.00/96.35% and 51.21/53.20/56.76%, respectively, in segmenting liver parenchyma and liver trauma regions. In volume-based severity analysis, the proposed model yields a linear regression relation of 0.95 in estimating the percentage of liver parenchyma disrupted by trauma. The model shows an accurate performance in avoiding false positives for patients without any liver parenchymal trauma. These results indicate that the model is generalizable on patients with pre-existing liver conditions, including fatty livers and congestive hepatopathy. Conclusion The proposed algorithms are able to accurately segment the liver and the regions affected by trauma. This pipeline demonstrates an accurate performance in estimating the percentage of liver parenchyma that is affected by trauma. Such a system can aid critical care medical personnel by providing a reproducible quantitative assessment of liver trauma as an alternative to the sometimes subjective AAST grading system that is used currently. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00759-9.
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Affiliation(s)
- Negar Farzaneh
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA. .,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA. .,Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Erica B Stein
- Department of Radiology, Michigan Medicine, Ann Arbor, MI, 48109, USA
| | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
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Rafiei S, Karimi N, Mirmahboub B, Najarian K, Felfeliyan B, Samavi S, Reza Soroushmehr SM. Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:6310-6313. [PMID: 31947285 DOI: 10.1109/embc.2019.8857835] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Automatic liver segmentation plays a vital role in computer-aided diagnosis or treatment. Manual segmentation of organs is a tedious and challenging task and is prone to human errors. In this paper, we propose innovative pre-processing and adaptive 3D region growing methods with subject-specific conditions. To obtain strong edges and high contrast, we propose effective contrast enhancement algorithm then we use the atlas intensity distribution of most probable voxels in probability maps along with location before designing conditions for our 3D region growing method. We also incorporate the organ boundary to restrict the region growing. We compare our method with the label fusion of 13 organs on state-of-the-art Deeds registration method and achieved Dice score of 92.56%.
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