Literature DB >> 23693135

Validation study of a fast, accurate, and precise brain tumor volume measurement.

Mong Dang1, Jayesh Modi, Mike Roberts, Christopher Chan, J Ross Mitchell.   

Abstract

UNLABELLED: Precision and accuracy are sometimes sacrificed to ensure that medical image processing is rapid. To address this, our lab had developed a novel level set segmentation algorithm that is 16× faster and >96% accurate on realistic brain phantoms.
METHODS: This study reports speed, precision and estimated accuracy of our algorithm when measuring MRIs of meningioma brain tumors and compares it to manual tracing and modified MacDonald (MM) ellipsoid criteria. A repeated-measures study allowed us to determine measurement precisions (MPs) - clinically relevant thresholds for statistically significant change.
RESULTS: Speed: the level set, MM, and trace methods required 1:20, 1:35, and 9:35 (mm:ss) respectively on average to complete a volume measurement (p<0.05). Accuracy: the level set was not statistically different to the estimated true lesion volumes (p>0.05). Precision: the MM's within-operator and between-operator MPs were significantly higher (worse) than the other methods (p<0.05). The observed difference in MP between the level set and trace methods did not reach statistical significance (p>0.05).
CONCLUSION: Our level set is faster on average than MM, yet has accuracy and precision comparable to manual tracing.
Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Brain tumor; Image processing; Magnetic resonance imaging; Segmentation; Volume

Mesh:

Year:  2013        PMID: 23693135     DOI: 10.1016/j.cmpb.2013.04.011

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  4 in total

1.  Three-dimensional Radiologic Assessment of Chemotherapy Response in Ewing Sarcoma Can Be Used to Predict Clinical Outcome.

Authors:  Maryam Aghighi; Justin Boe; Jarrett Rosenberg; Rie Von Eyben; Rakhee S Gawande; Philippe Petit; Tarsheen K Sethi; Jeremy Sharib; Neyssa M Marina; Steven G DuBois; Heike E Daldrup-Link
Journal:  Radiology       Date:  2016-03-16       Impact factor: 11.105

2.  What are the true volumes of SEGA tumors? Reliability of planimetric and popular semi-automated image segmentation methods.

Authors:  Konrad Stawiski; Joanna Trelińska; Dobromiła Baranska; Iwona Dachowska; Katarzyna Kotulska; Sergiusz Jóźwiak; Wojciech Fendler; Wojciech Młynarski
Journal:  MAGMA       Date:  2017-03-20       Impact factor: 2.310

3.  Children With Intracranial Arachnoid Cysts: Classification and Treatment.

Authors:  Zhen Tan; Yongxin Li; Fengjun Zhu; Dongdong Zang; Cailei Zhao; Cong Li; Dan Tong; Heye Zhang; Qian Chen
Journal:  Medicine (Baltimore)       Date:  2015-11       Impact factor: 1.889

4.  Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

Authors:  Joseph Ross Mitchell; Konstantinos Kamnitsas; Kyle W Singleton; Scott A Whitmire; Kamala R Clark-Swanson; Sara Ranjbar; Cassandra R Rickertsen; Sandra K Johnston; Kathleen M Egan; Dana E Rollison; John Arrington; Karl N Krecke; Theodore J Passe; Jared T Verdoorn; Alex A Nagelschneider; Carrie M Carr; John D Port; Alice Patton; Norbert G Campeau; Greta B Liebo; Laurence J Eckel; Christopher P Wood; Christopher H Hunt; Prasanna Vibhute; Kent D Nelson; Joseph M Hoxworth; Ameet C Patel; Brian W Chong; Jeffrey S Ross; Jerrold L Boxerman; Michael A Vogelbaum; Leland S Hu; Ben Glocker; Kristin R Swanson
Journal:  J Med Imaging (Bellingham)       Date:  2020-10-16
  4 in total

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