Purpose Our aim is to conduct analysis and comparison of some methods commonly used to measure the volume of hematoma, for example, slice method, voxelization method, and 3D-Slicer software method (projection method). Method In order to validate the accuracy of the slice method, voxelization method, and 3D-Slicer method, these three methods were first applied to measure two known volumetric models, respectively. Then, a total of 198 patients diagnosed with spontaneous intracerebral hemorrhage (ICH) were recruited. The patients were split into 3 different groups based on the hematoma size: group 1: volume < 10 ml (n = 89), group 2: volume between 10 and 20 ml (n = 59), and group 3: volume > 20 ml (n = 50). And the shape of the hematoma was classed into regular (round to ellipsoid) with smooth margins (n = 76), irregular with frayed margins (n = 85), and multilobular (n = 37). The slice method, voxelization method, and 3D-Slicer method were adopted to measure the volume of hematoma, respectively, considering the nonclosed models and the models which may contain inaccurate normal information during CT scan. Moreover, the results were compared with the 3D-Slicer method for closed models. Results There was a significant estimation error (P < 0.05) using these three methods to calculate the volume of the closed hematoma model. The estimated hematoma volume was calculated to be 14.2086743 ± 0.900559087 ml, 14.2119130 ± 0.900851812 ml, and 14.2123825 ± 0.900835916 ml using slice method 1, slice method 2, and the voxelization method, respectively, compared to 14.212656 ± 0.900992371 ml using the 3D-Slicer method. The mean estimation error was -0.00398172 ml, -0.00074303 ml, and -0.00027354 ml caused by slice method 1, slice method 2, and voxelization method, respectively. There was a significant estimation error (P < 0.05), applying these three methods to calculate the volume of the nonclosed hematoma model. The estimated hematoma volume was calculated to be 14.1928246 ± 0.902210314 ml using the 3D-Slicer method. The mean estimation error was calculated to be -0.00402121 ml, -0.00078237 ml, -0.00031288 ml, and -0.01983136 ml using slice method 1, slice method 2, voxelization method, and 3D-Slicer method, respectively. Conclusions The 3D-Slicer software method is considered as a stable and capable method of high precision for the calculation of a closed hematoma model with correct normal direction, while it would be inappropriate for the nonclosed model nor the model with incorrect normal direction. The slice method and voxelization method can be the supplement and improvement of the 3D-Slicer software method, for the purpose of achieving precision medicine.
[1]
Stephen R Bowen,et al.
Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization
,
2017,
Journal of medical imaging.
[2]
C. Anderson,et al.
Guidelines for the Management of Spontaneous Intracerebral Hemorrhage: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association
,
2010,
Stroke.
[3]
S. Sacco,et al.
Incidence and 10-Year Survival of Intracerebral Hemorrhage in a Population-Based Registry
,
2009,
Stroke.
[4]
Catherine Perry,et al.
Impact of centralising acute stroke services in English metropolitan areas on mortality and length of hospital stay: difference-in-differences analysis
,
2014,
BMJ : British Medical Journal.
[5]
Raúl San José Estépar,et al.
Three-dimensional Printing and 3D Slicer: Powerful Tools in Understanding and Treating Structural Lung Disease.
,
2016,
Chest.
[6]
Milan Sonka,et al.
3D Slicer as an image computing platform for the Quantitative Imaging Network.
,
2012,
Magnetic resonance imaging.
[7]
J. Broderick,et al.
Volume of Intracerebral Hemorrhage: A Powerful and Easy‐to‐Use Predictor of 30‐Day Mortality
,
1993,
Stroke.
[8]
Michael Tin Chung Poon,et al.
Long-term prognosis after intracerebral haemorrhage: systematic review and meta-analysis
,
2013,
Journal of Neurology, Neurosurgery & Psychiatry.
[9]
Fausto Milletari,et al.
TOMAAT: volumetric medical image analysis as a cloud service
,
2018,
ArXiv.
[10]
Zaiyi Liu,et al.
MR diffusion-weighted imaging-based subcutaneous tumour volumetry in a xenografted nude mouse model using 3D Slicer: an accurate and repeatable method
,
2015,
Scientific Reports.
[11]
Bainan Xu,et al.
Comparison of the Tada Formula With Software Slicer: Precise and Low-Cost Method for Volume Assessment of Intracerebral Hematoma
,
2014,
Stroke.
[12]
James V. Miller,et al.
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
,
2013,
Scientific Reports.