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.
BMC Med Imaging. 2022 Mar 8;22(1):39. doi: 10.1186/s12880-022-00759-9.
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.
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.
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.
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.
肝外伤的早期检测和严重程度评估对创伤患者的最佳分诊和管理至关重要。目前的创伤方案采用计算机断层扫描（CT）评估损伤，这种方法是主观的和定性的（而不是定量的），这两者的缺陷都可以通过能够生成实时可重复和定量信息的自动计算机辅助系统来解决。本研究概述了计算肝外伤严重程度的重要指标-创伤后肝实质破坏百分比的端到端流水线，这是美国外科创伤协会（AAST）肝损伤分级的主要工具。
该框架包括深度卷积神经网络，首先使用三维对比增强 CT 扫描生成肝实质（包括正常和受影响的肝）和受创伤影响的区域的初始掩模。接下来，在后处理步骤中，将关于肝外伤位置和强度分布的人类领域知识整合到模型中，以避免出现假阳性区域。生成肝实质和外伤掩模后，计算相应的体积。然后计算肝实质破坏程度，即受外伤破坏的肝实质体积。
该模型在密歇根大学健康系统（UMHS）的内部数据集上进行了训练和验证，包括 77 例 CT 扫描（34 例有肝实质外伤，43 例无肝实质外伤）。所提出的分割模型的 Dice/召回/精度系数分别为 96.13%/96.00%/96.35%和 51.21%/53.20%/56.76%，分别用于分割肝实质和肝外伤区域。在基于体积的严重程度分析中，所提出的模型在估计肝实质破坏百分比方面具有 0.95 的线性回归关系。该模型在避免无肝实质外伤患者的假阳性方面表现出准确的性能。这些结果表明，该模型在包括脂肪肝和充血性肝病变在内的具有预先存在的肝脏疾病的患者中具有通用性。
所提出的算法能够准确地分割肝脏和受外伤影响的区域。该流水线在估计肝实质受外伤影响的百分比方面表现出准确的性能。这样的系统可以通过提供肝外伤的可重复定量评估，作为当前使用的有时主观的 AAST 分级系统的替代方法，为重症监护医务人员提供帮助。
