What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Internal medicine
The scientist’s investigation covers issues in Lung, Artificial intelligence, Computer vision, Segmentation and Tomography.
His Lung research includes themes of Image processing, Nuclear medicine, Radiology, Pulmonary function testing and Respiratory system.
His Artificial intelligence study integrates concerns from other disciplines, such as Retinal and Pattern recognition.
His work deals with themes such as Classifier, Bronchus, Airway wall and Atlas, which intersect with Computer vision.
Joseph M. Reinhardt studies Image segmentation, a branch of Segmentation.
The Tomography study which covers Airway that intersects with Computed tomography, Volume, Computational model, Tree and Geometry.
His most cited work include:
- Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images (816 citations)
- Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge (330 citations)
- CT-based geometry analysis and finite element models of the human and ovine bronchial tree (253 citations)
What are the main themes of his work throughout his whole career to date?
Joseph M. Reinhardt focuses on Artificial intelligence, Computer vision, Image registration, Lung and Segmentation.
His study ties his expertise on Pattern recognition together with the subject of Artificial intelligence.
His studies deal with areas such as Retinal, Fundus and Airway as well as Computer vision.
Joseph M. Reinhardt has researched Image registration in several fields, including Lung ventilation, Nuclear medicine, Biomedical engineering and Medical imaging.
In his work, Lung cancer and Pathology is strongly intertwined with Radiology, which is a subfield of Lung.
The Segmentation study combines topics in areas such as Lung lobe, Tomography and Graph.
He most often published in these fields:
- Artificial intelligence (37.50%)
- Computer vision (30.00%)
- Image registration (27.92%)
What were the highlights of his more recent work (between 2015-2021)?
- Lung (24.17%)
- Internal medicine (7.92%)
- Cardiology (7.50%)
In recent papers he was focusing on the following fields of study:
Joseph M. Reinhardt mainly investigates Lung, Internal medicine, Cardiology, Image registration and COPD.
His Lung research is multidisciplinary, incorporating elements of Lung cancer and Radiology.
His Cardiology research incorporates elements of Pulmonary disease and Airway, Airway disease.
Image registration is a primary field of his research addressed under Artificial intelligence.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Tissue expansion and Computer vision.
Joseph M. Reinhardt interconnects Transfer of learning and Medical imaging in the investigation of issues within Segmentation.
Between 2015 and 2021, his most popular works were:
- FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images (43 citations)
- Computed Tomography Measure of Lung at Risk and Lung Function Decline in Chronic Obstructive Pulmonary Disease. (34 citations)
- COPDGene® 2019: Redefining the Diagnosis of Chronic Obstructive Pulmonary Disease. (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Computer vision
His main research concerns Spirometry, COPD, Computed tomography, Internal medicine and Lung.
His work investigates the relationship between Computed tomography and topics such as Voxel that intersect with problems in Nuclear medicine, Lung tissue and Radiation dose.
He works mostly in the field of Internal medicine, limiting it down to topics relating to Cardiology and, in certain cases, Airway and Surgery.
In his study, which falls under the umbrella issue of Lung, Pattern recognition is strongly linked to Multi resolution.
The concepts of his Tomography study are interwoven with issues in Deep learning and Image segmentation.
His Image segmentation research is included under the broader classification of Segmentation.
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