Multi-object Model-Based Multi-atlas Segmentation Constrained Grid Cut for Automatic Segmentation of Lumbar Vertebrae from CT Images

2018 ◽  
pp. 65-71 ◽  
Author(s):  
Weimin Yu ◽  
Wenyong Liu ◽  
Liwen Tan ◽  
Shaoxiang Zhang ◽  
Guoyan Zheng
Keyword(s):  
Automatic Segmentation ◽  
Lumbar Vertebrae ◽  
Ct Images ◽  
Object Model ◽  
Model Based ◽  
Atlas Segmentation

scholarly journals Deep Learning based Segmentation of Lumbar Vertebrae from CT Images

10.29007/vt7v ◽  
2018 ◽  
Author(s):  
Rens Janssens ◽  
Guoyan Zheng
Keyword(s):  
Automatic Segmentation ◽  
Lumbar Vertebrae ◽  
Ct Images ◽  
Lumbar Region ◽  
Dice Coefficient ◽  
Convolutional Networks ◽  
Surface Distance ◽  
Fully Convolutional Networks ◽  
Symmetric Surface ◽  
Class Segmentation

We present a method to address the challenging problem of automatic segmentation of lumbar vertebrae from CT images acquired with varying fields of view. Our method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting of a localization FCN and a segmentation FCN. More specifically, in the first step we train a regression 3D FCN (we call it “LocalizationNet”) to find the bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it “SegmentationNet”) is then developed, which after training, can perform a pixel-wise multi-class segmentation to map a cropped lumber region volumetric data to its volume-wise labels. Evaluated on publicly available datasets, our method achieved an average Dice coefficient of 95.77 ± 0.81% and an average symmetric surface distance of 0.37 ± 0.06 mm.

scholarly journals Fully Automatic Segmentation and Landmarking of Hip CT Images

10.29007/ds5r ◽  
2020 ◽  
Author(s):  
Guoyan Zheng
Keyword(s):  
Automatic Segmentation ◽  
Ct Images ◽  
Generic Models ◽  
Mean Error ◽  
Segmentation Accuracy ◽  
Fully Automatic ◽  
Ct Data ◽  
Two Stages ◽  
Atlas Segmentation ◽  
Acetabular Rim

We present a fully automatic method of segmenting and landmarking hip CT images for planning of Total Hip Arthroplasty (THA). Our method consists of two stages, i.e., the segmentation stage and the landmarking stage. At the segmentation stage, a multi-atlas segmentation constrained graph method is employed to fully automatically segment both the pelvis and the bilateral proximal femurs from the input CT data. The segmentation stage is followed by the landmarking stage, where a set of pre-defined landmarks are transferred from generic models of the associated hip structures to the input CT space via non-rigid registrations in order to compute a set of functional parameters that are relevant to planning of THA. Evaluated on 20 hip patients, we computed both the segmentation accuracy and the landmarking accuracy. An average segmentation error of 0.38 ± 0.25 mm and 0.49 ± 0.22 mm was found for the hemi-pelvis and for the proximal femurs, respectively. For 3D landmarking, a mean error of 1.58 ± 0.87 mm and 0.46 ± 0.39 mm was found for the acetabular rim center and the acetabular rim radius, respectively; a mean error of 0.74±0.45o was found for the orientation of the anterior pelvic plane; and a mean error of 3.14 ± 1.90 mm and 2.04 ± 1.61 mm was found for the femoral head center and the femoral offset, respectively.

Automatic segmentation of lumbar vertebrae in CT images

2017 ◽  
Author(s):  
Amruta Kulkarni ◽  
Akshita Raina ◽  
Mona Sharifi Sarabi ◽  
Christine S. Ahn ◽  
Diana Babayan ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Lumbar Vertebrae ◽  
Ct Images

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

Automatic Segmentation of COVID-19 CT Images using improved MultiResUNet

2020 ◽  
Author(s):  
Qi Yang ◽  
Yunke Li ◽  
Mengyi Zhang ◽  
Tian Wang ◽  
Fei Yan ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ct Images

scholarly journals Reconstruction and finite element analysis of brain hemangioma model based on CT images

2018 ◽  
Vol 1064 ◽  
pp. 012049
Author(s):  
QingFei Jiang ◽  
XueYan Ma ◽  
SiYu Wang ◽  
Kai Yang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Ct Images ◽  
Element Analysis ◽  
Model Based

Gout in the Axial Skeleton

2009 ◽  
Vol 36 (3) ◽  
pp. 609-613 ◽  
Author(s):  
RUKMINI M. KONATALAPALLI ◽  
PAUL J. DEMARCO ◽  
JAMES S. JELINEK ◽  
MARK MURPHEY ◽  
MICHAEL GIBSON ◽  
...  
Keyword(s):  
Facet Joint ◽  
Case Reports ◽  
Case Series ◽  
Lumbar Vertebrae ◽  
Axial Skeleton ◽  
Ct Images ◽  
Common Finding ◽  
Appendicular Skeleton ◽  
Pars Interarticularis ◽  
A Prospective Study

Objective.Gout typically affects the peripheral joints of the appendicular skeleton and rarely involves the axial joints. The literature on axial gout is limited to case reports and case series. This preliminary study was conducted to identify the frequency and characteristics of axial gout.Methods.Six hundred thirty medical records with ICD codes 274.0, 274.82, and 274.9 for peripheral gout were reviewed. Ninety-two patients had clinical or crystal-proven gout, of which 64 had prior computed tomography (CT) images of the spine performed for various medical reasons. These CT images were reviewed for features of axial gout, which include vertebral erosions mainly at the discovertebral junction and the facet joints, deposits of tophi, and erosions in the vertebral body, epidural space, ligamentum flavum and pars interarticularis.Results.Nine of the 64 patients had radiographic changes suggestive of axial gout. Lumbar vertebrae were most commonly involved, with facet joint erosions being the most common finding. Isolated involvement of the sacroiliac joints was seen in 2 patients. Axial gout had been diagnosed clinically in only one patient.Conclusion.Radiologic changes of axial gout were more common than recognized clinically, with a frequency of 14%. Since not all patients had CT images, it is possible that the frequency of axial involvement was even greater. A prospective study is needed to further define this process.

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