A Robust Segmentation Framework for Spine Trauma Diagnosis

2014 ◽  
pp. 25-33
Author(s):  
Poay Hoon Lim ◽  
Ulas Bagci ◽  
Li Bai
Keyword(s):  
Spine Trauma ◽  
Robust Segmentation ◽  
Segmentation Framework

A robust segmentation framework for closely packed buildings from airborne LiDAR point clouds

2020 ◽  
Vol 41 (14) ◽  
pp. 5147-5165 ◽  
Author(s):  
Xinsheng Wang ◽  
Ting On Chan ◽  
Kai Liu ◽  
Jun Pan ◽  
Ming Luo ◽  
...  
Keyword(s):  
Point Clouds ◽  
Airborne Lidar ◽  
Robust Segmentation ◽  
Segmentation Framework

Gray Area: Methylprednisolone in Spine Trauma

2006 ◽  
Vol 2 (12) ◽  
pp. 23
Author(s):  
BARBARA J. RUTLEDGE
Keyword(s):  
Spine Trauma ◽  
Gray Area

Multiple subluxations and comminuted fracture of the cervical spine in a sheep

2015 ◽  
Vol 43 (01) ◽  
pp. 44-38
Author(s):  
C.-C. Lin ◽  
K.-S. Chen ◽  
Y.-L. Lin ◽  
J. P.-W. Chan
Keyword(s):  
Cervical Spine ◽  
Pain Perception ◽  
Cervical Vertebrae ◽  
Traumatic Injury ◽  
Spinal Reflexes ◽  
Spine Trauma ◽  
Blunt Force Trauma ◽  
Comminuted Fracture ◽  
History Of ◽  
The Right

SummaryA 5-month-old, 13.5 kg, female Corriedale sheep was referred to the Veterinary Medicine Teaching Hospital, with a history of traumatic injury of the cervical spine followed by non-ambulatoric tetraparesis that occurred 2 weeks before being admitted to the hospital. At admission, malalignment of the cervical spine with the cranial part of the neck deviating to the right was noted. Neurological examinations identified the absence of postural reactions in both forelimbs, mildly decreased spinal reflexes, and normal reaction to pain perception tests. Radiography revealed malalignment of the cervical vertebrae with subluxations at C1–C2 and C2–C3, and a comminuted fracture of the caudal aspect of C2. The sheep was euthanized due to a presumed poor prognosis. Necropsy and histopathological findings confirmed injuries of the cervical spine from C1 to C3, which were consistent with the clinical finding of tetraparesis in this case. This paper presents a rare case of multiple subluxations of the cervical spine caused by blunt force trauma in a young sheep. These results highlight the importance of an astute clinical diagnosis for such an acute cervical spine trauma and the need for prompt surgical correction for similar cases in the future.

scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

scholarly journals A Robust Segmentation Algorithmfor for 3D Complex Meshes

2021 ◽  
Vol 1802 (3) ◽  
pp. 032045
Author(s):  
Yu Hou ◽  
Yong Zhao
Keyword(s):  
Robust Segmentation

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

scholarly journals EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking

2021 ◽  
Vol 27 (5) ◽  
pp. 2757-2767
Author(s):  
Rakshit S. Kothari ◽  
Aayush K. Chaudhary ◽  
Reynold J. Bailey ◽  
Jeff B. Pelz ◽  
Gabriel J. Diaz
Keyword(s):  
Gaze Tracking ◽  
Segmentation Framework

Toward Developing a Specific Outcome Instrument for Spine Trauma

Spine ◽  
2015 ◽  
Vol 40 (17) ◽  
pp. 1371-1379 ◽  
Author(s):  
F. Cumhur Oner ◽  
Said Sadiqi ◽  
A. Mechteld Lehr ◽  
Bizhan Aarabi ◽  
Robert N. Dunn ◽  
...  
Keyword(s):  
Spine Trauma ◽  
Outcome Instrument ◽  
Specific Outcome
Sign in / Sign up

Export Citation Format

Share Document