Computer-assisted quantification of the skull deformity for craniosynostosis from 3D head CT images using morphological descriptor and hierarchical classification

2017 ◽  
Author(s):  
Min Jin Lee ◽  
Helen Hong ◽  
Kyu Won Shim ◽  
Yong Oock Kim
2020 ◽  
Vol 2 (1) ◽  
pp. 004-008
Author(s):  
Asha K Kumaraswamy ◽  
Chandrashekar Patil

Contrast-enhanced Computed Tomography (CT) imaging is most useful tool in diagnosing and locating the kidney lesions. An automated kidney and tumor segmentation are very helpful because it can provide the precise information about the location and size of lesions which can be used in quantitative analysis of the tumor. Semantic segmentation of kidney is very challenging as it requires large dataset for training and its morphological heterogeneity makes it a difficult problem. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) has publicly released a 210 cross sectional CT images with kidney tumors along with corresponding semantic segmentation masks. In this work we proposed a novel two stage 2D segmentation method to automatically segment kidney and tumor using the combination of Unet++ and squeeze and excite approach. The proposed network is trained in keras framework. Our method achieves a dice score of 0.98 and 0.965 with kidney and tumor respectively on training data and the results demonstrates the accuracy of our proposed method. Proposed method was able to segment kidney and tumor from abdominal CT images which can provide the exact location and size of the tumor. This information can also be used to analyze treatment response.


2013 ◽  
Vol 25 (03) ◽  
pp. 1350033 ◽  
Author(s):  
Ke-Chun Huang ◽  
Chun-Chih Liao ◽  
Furen Xiao ◽  
Charles Chih-Ho Liu ◽  
I-Jen Chiang ◽  
...  

The volume of the skull defect should be one of the most important quantitative measures for decompressive craniectomy. However, there has been no study focusing on automated estimation of the volume from postoperative computed tomography (CT). This study develops and validates three methods that can automatically locate, recover and measure the missing skull region based on symmetry without preoperative images. The low resolution estimate (LRE) method involves downsizing CT images, finding the axis of symmetry for each slice, and estimating the location and size of the missing skull regions. The intact mid-sagittal plane (iMSP) can be defined either by dimension-by-dimension (DBD) method as a global symmetry plane or by Liu's method as a regression from each slices. The skull defect volume can then be calculated by skull volume difference (SVD) with respect to each iMSP. During a 48-month period between July 2006 and June 2010 at a regional hospital in northern Taiwan, we collected 30 sets of nonvolumetric CT images after craniectomies. Three board-certified neurosurgeons perform computer-assisted volumetric analysis of skull defect volume V Man as the gold standard for evaluating the performance of our algorithm. We compare the error of the three volumetry methods. The error of V LRE is smaller than that of V Liu (p < 0.0001) and V DBD (p = 0.034). The error of V DBD is significant smaller than that of V Liu (p = 0.001). The correlation coefficients between V Man and V LRE , V Liu , V DBD are 0.98, 0.88 and 0.95, respectively. In conclusion, these methods can help to define the skull defect volume in postoperative images and provide information of the immediate volume gain after decompressive craniectomies. The iMSP of the postoperative skull can be reliably identified using the DBD method.


2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Petr Martynov ◽  
Nikolai Mitropolskii ◽  
Katri Kukkola ◽  
Monika Gretsch ◽  
Vesa-Matti Koivisto ◽  
...  
Keyword(s):  

Author(s):  
Yonghong Li ◽  
Jianhuang Wu ◽  
Zhijun Chen ◽  
Fucang Jia ◽  
Qingmao Hu

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