Improving airway segmentation in computed tomography using leak detection with convolutional networks

Medical Image Analysis

Volumn 36, Issue , 2017, Pages 52-60

  Charbonnier, Jean Paul ^a   Rikxoort, Eva M van ^a   Setio, Arnaud A A ^a   Schaefer Prokop, Cornelia M ^a,b   Ginneken, Bram van ^a   Ciompi, Francesco ^a  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b MEANDER MEDICAL CENTER   (Netherlands)

Author keywords

Airway segmentation; Chest computed tomography; Convolutional networks

Indexed keywords

CONVOLUTION; LEAK DETECTION; PARAMETER ESTIMATION; TREES (MATHEMATICS);

AIRWAY TREES; CLASSIFICATION TASKS; CONVOLUTIONAL NETWORKS; FALSE POSITIVE RATES; INDEPENDENT SET; MULTIPLE SEGMENTATION; SEGMENTATION ALGORITHMS; STATE-OF-THE-ART METHODS;

COMPUTERIZED TOMOGRAPHY;

AIRWAY SEGMENTATION LEAK DETECTION; ARTICLE; ARTIFACT REDUCTION; CLASSIFICATION ALGORITHM; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; CONVOLUTIONAL NETWORK; FALSE POSITIVE RESULT; HUMAN; IMAGE QUALITY; IMAGE SEGMENTATION; PRIORITY JOURNAL; THORAX RADIOGRAPHY; TRACHEOBRONCHIAL TREE; ALGORITHM; DIAGNOSTIC IMAGING; PROCEDURES; REPRODUCIBILITY; RESPIRATORY SYSTEM; THORAX; X-RAY COMPUTED TOMOGRAPHY;

ALGORITHMS; HUMANS; REPRODUCIBILITY OF RESULTS; RESPIRATORY SYSTEM; THORAX; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84995466936     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2016.11.001     Document Type: Article

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