Dr. Andinet Enquobahrie Profile

Dr. Andinet Enquobahrie

Assistant Director at Kitware Inc

SPIE Involvement:

Conference Program Committee | Author

Publications (16)

PROCEEDINGS ARTICLE | March 13, 2018

Osteotomy planner: an open-source tool for osteotomy simulation

Proc. SPIE. 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Data modeling, Visualization, Surgery, Databases, Computer simulations, 3D modeling, Bone, Skull, 3D vision, 3D image processing

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PROCEEDINGS ARTICLE | March 13, 2018

High fidelity virtual reality orthognathic surgery simulator

Proc. SPIE. 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Data modeling, Visualization, Tissues, Surgery, Computer simulations, Haptic technology, Bone, Virtual reality, Electromagnetic simulation, Device simulation

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PROCEEDINGS ARTICLE | February 27, 2018

Radiation-free quantification of head malformations in craniosynostosis patients from 3D photography

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Principal component analysis, Surgery, Image segmentation, Photography, Bone, Head, Shape analysis, Computed tomography, Binary data, 3D image processing

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PROCEEDINGS ARTICLE | March 3, 2017

Temporal bone dissection simulator for training pediatric otolaryngology surgeons

Proc. SPIE. 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Human-machine interfaces, Nerve, Surgery, Image segmentation, Computer simulations, Haptic technology, Bone, Computed tomography, Virtual reality, Prototyping

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PROCEEDINGS ARTICLE | March 3, 2017

Physiology informed virtual surgical planning: a case study with a virtual airway surgical planner and BioGears

Proc. SPIE. 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Carbon dioxide, Data modeling, Visualization, Surgery, Pathology, Resistance, 3D modeling, Lung, Oxygen, Computed tomography, Physiology, Virtual reality, Biomedical engineering, Human physiology

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PROCEEDINGS ARTICLE | March 12, 2014

A user-friendly automated port placement planning system for laparoscopic robotic surgery

Proc. SPIE. 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Visualization, Surgery, Interfaces, Robotics, Medical imaging, Robotic systems, 3D visualizations, 3D image processing, Laparoscopy, Robotic surgery

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PROCEEDINGS ARTICLE | March 29, 2013

3D of brain shape and volume after cranial vault remodeling surgery for craniosynostosis correction in infants

Proc. SPIE. 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Surgery, Magnetic resonance imaging, Image segmentation, Control systems, 3D modeling, Shape analysis, Computed tomography, Skull, Neuroimaging, Brain

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PROCEEDINGS ARTICLE | March 15, 2013

Accelerating ultrasound image analysis research through publically available database

Proc. SPIE. 8675, Medical Imaging 2013: Ultrasonic Imaging, Tomography, and Therapy

KEYWORDS: Liver, Ultrasonography, Databases, Magnetic resonance imaging, Image processing, Scanners, Image acquisition, Image analysis, Image registration, Data acquisition

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PROCEEDINGS ARTICLE | March 14, 2013

Patient-specific port placement for laparoscopic surgery using atlas-based registration

Proc. SPIE. 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: 3D acquisition, Detection and tracking algorithms, Data modeling, Visualization, Surgery, Cameras, 3D modeling, Image registration, Medical imaging, Laparoscopy

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PROCEEDINGS ARTICLE | February 17, 2012

Neurosurgery simulation using non-linear finite element modeling and haptic interaction

Proc. SPIE. 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Optical spheres, Tissues, Surgery, Computer simulations, Haptic technology, Finite element methods, Virtual reality, Performance modeling, Systems modeling, Brain

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PROCEEDINGS ARTICLE | February 17, 2012

Application of unscented Kalman filter for robust pose estimation in image-guided surgery

Proc. SPIE. 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Sensors, Magnetic resonance imaging, Error analysis, Image analysis, Navigation systems, Optical tracking, Computed tomography, Motion models, Filtering (signal processing), Image-guided intervention

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PROCEEDINGS ARTICLE | February 27, 2010

Reducing depth uncertainty in large surgical workspaces, with applications to veterinary medicine

Proc. SPIE. 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling

KEYWORDS: Medicine, Visualization, Surgery, Sensors, 3D modeling, Image registration, Optical tracking, Computed tomography, Image-guided intervention, Instrument modeling

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PROCEEDINGS ARTICLE | March 13, 2009

An open-source framework for testing tracking devices using Lego Mindstorms

Proc. SPIE. 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling

KEYWORDS: Optical sensors, Sensors, Calibration, Robotics, Computer programming, Telecommunications, Software development, Servomechanisms, C++, Image-guided intervention

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PROCEEDINGS ARTICLE | March 13, 2008

Integration of a real-time video grabber component with the open source image-guided surgery toolkit IGSTK

Proc. SPIE. 6919, Medical Imaging 2008: PACS and Imaging Informatics

KEYWORDS: 3D video streaming, Visualization, Surgery, Imaging systems, Ultrasonography, Video, Medical imaging, Computed tomography, Image-guided intervention, 3D image processing

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PROCEEDINGS ARTICLE | March 21, 2007

The Image-Guided Surgery ToolKit IGSTK: an open source C++ software toolkit

Proc. SPIE. 6516, Medical Imaging 2007: PACS and Imaging Informatics

KEYWORDS: Logic, Safety, Visualization, Control systems, Computer programming, Software development, Optical tracking, Open source software, C++, Image-guided intervention

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PROCEEDINGS ARTICLE | May 12, 2004

Automated detection of pulmonary nodules from whole lung helical CT scans: performance comparison for isolated and attached nodules

Proc. SPIE. 5370, Medical Imaging 2004: Image Processing

KEYWORDS: Image processing algorithms and systems, Chest, Detection and tracking algorithms, Databases, Image segmentation, Lung, Solids, Computed tomography, Lung cancer, Algorithm development

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The objective of this research is to evaluate and compare the performance of our automated detection algorithm on isolated and attached nodules in whole lung CT scans. Isolated nodules are surrounded by the lung parenchyma with no attachment to large solid structures such as the chest wall or mediastinum surface, while attached nodules are adjacent to these structures. The detection algorithm involves three major stages. First, the region of the image space where pulmonary nodules are to be found is identified. This involves segmenting the lung region and generating the pleural surface. In the second stage, which is the hypothesis generation stage, nodule candidate locations are identified and their sizes are estimated. The nodule candidates are successively refined in the third stage a sequence of filters of increasing complexity. The algorithm was tested on a dataset containing 250 low-dose whole lung CT scans with 2.5mm slice thickness. A scan is composed of images covering the whole lung region for a single person. The dataset was partitioned into 200 and 50 scans for training and testing the algorithm. Only solid nodules were considered in this study. Experienced chest radiologists identified a total of 447 solid nodules. 345 and 102 of the nodules were from the training and testing datasets respectively. 126(28.2%) of the nodules in the dataset were attached nodules. The detection performance was then evaluated separately for isolated and attached nodule types considering different size ranges. For nodules 3mm and larger, the algorithm achieved a sensitivity of 97.8% with 2.0 false positives (FPs) per scan and 95.7% with 19.3 FPs per scan for isolated and attached nodules respectively. For nodules 4mm and larger, a sensitivity of 96.6% with 1.5 FP per scan and a 100% sensitivity with 13 FPs per scan were obtained for isolated and attached nodule types respectively. The results show that our algorithm detects isolated and attached nodules with comparable sensitivity but differing number of false positives per scan. The high number of false positives for attached nodule detection was mainly due to the complexity of the mediastinum lung surface.

Showing 5 of 16 publications

Conference Committee Involvement (3)

Digital Pathology

25 February 2015 | Orlando, Florida, United States

Digital Pathology

16 February 2014 | San Diego, California, United States

Digital Pathology

10 February 2013 | Lake Buena Vista (Orlando Area), Florida, United States

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