Computer-assisted quantification of surgical outcome in infants with sagittal craniosynostosis in 3D head CT images using mean normal skull model

2020 ◽  
Author(s):  
Min Jin Lee ◽  
Helen Hong ◽  
Kyu Won Shim
Keyword(s):  
Surgical Outcome ◽  
Ct Images ◽  
Computer Assisted ◽  
Head Ct ◽  
Sagittal Craniosynostosis ◽  
Skull Model

scholarly journals Cranial Bone Growth in Isolated Sagittal Craniosynostosis Compared to Normal Growth in the First Six Months of Age

2019 ◽  
Author(s):  
Ezgi Mercan ◽  
Richard A. Hopper ◽  
A. Murat Maga
Keyword(s):  
Growth Model ◽  
Surgical Outcome ◽  
Bone Growth ◽  
Bone Fractures ◽  
Ct Images ◽  
Growth Patterns ◽  
Cranial Bone ◽  
Control Group ◽  
Anatomical Landmarks ◽  
Sagittal Craniosynostosis

AbstractBackgroundSagittal craniosynostosis (SCS), the most common type of premature perinatal cranial suture fusion, results in abnormal head shape that requires extensive surgery to correct. It is important to find objective and repeatable measures of severity and surgical outcome to examine the effect of timing and technique on different SCS surgeries. The purpose of this study was to develop statistical models of infant (0-6 months old) skull growth in both normative and SCS subjects (prior to surgery). Our goal was to apply these models to the assessment of differences between these two groups in overall post-natal growth patterns and sutural growth rates as a first step to develop methods for predictive models of surgical outcome.Methods and Findings:We identified 81 patients with isolated, non-syndromic SCS from Seattle Children’s Craniofacial Center patient database who had a pre-operative CT exam before the age of six months. As a control group, we identified 117 CT exams without any craniofacial abnormalities or bone fractures in the same age group. We first created population-level templates from the CT images of the SCS and normal groups. All CT images from both groups, as well as the canonical templates of both cohorts were annotated with anatomical landmarks, which were used in a growth model that predicted the locations of these landmarks at a given age based on each population. Using the template images and the landmark positions predicted by the growth models, we created 3D meshes for each week of age up to six months for both populations. To analyze the growth patterns at the suture sites, we annotated both templates with additional semi-landmarks equally spaced along the metopic, coronal, sagittal and lambdoidal cranial sutures. By transferring these semi-landmarks to meshes produced from the growth model, we measured the displacement of the bone borders and suture closure rates. We found that the growth at the metopic and coronal sutures were more rapid in the SCS cohort compared to the normal cohort. The antero-posterior displacement of the semi-landmarks indicated a more rapid growth in the sagittal plane in the SCS model compared to the normal model as well.Conclusions:Statistical templates and geometric morphometrics are promising tools for understanding the growth patterns in normal and synostotic populations and to produce objective and reproducible measurements of severity and outcome. Our study is the first of its kind to quantify the bone growth for the first six months of life in both normal and sagittal synostosis patients.

Cascaded U-net for Kidney and Tumor Segmentation from CT volumes

2020 ◽  
Vol 2 (1) ◽  
pp. 004-008
Author(s):  
Asha K Kumaraswamy ◽  
Chandrashekar Patil
Keyword(s):  
Semantic Segmentation ◽  
Ct Images ◽  
Difficult Problem ◽  
Training Data ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Kidney Tumors ◽  
Cross Sectional ◽  
Morphological Heterogeneity ◽  
Enhanced Computed Tomography

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.

AUTOMATED VOLUMETRY OF POSTOPERATIVE SKULL DEFECT ON BRAIN CT

2013 ◽  
Vol 25 (03) ◽  
pp. 1350033 ◽  
Author(s):  
Ke-Chun Huang ◽  
Chun-Chih Liao ◽  
Furen Xiao ◽  
Charles Chih-Ho Liu ◽  
I-Jen Chiang ◽  
...  
Keyword(s):  
Sagittal Plane ◽  
Symmetry Plane ◽  
Correlation Coefficients ◽  
Volumetric Analysis ◽  
Ct Images ◽  
Computer Assisted ◽  
Global Symmetry ◽  
Skull Defect ◽  
Defect Volume ◽  
Northern Taiwan

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.

scholarly journals Testing of the assisting software for radiologists analysing head CT images: lessons learned

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