Literature DB >> 30343401

Can the spherical gold standards be used as an alternative to painted gold standards for the computerized detection of lesions using voxel-based classification?

Yukihiro Nomura1, Naoto Hayashi2, Shouhei Hanaoka3, Tomomi Takenaga2, Mitsutaka Nemoto4, Soichiro Miki2, Takeharu Yoshikawa2, Osamu Abe3.   

Abstract

PURPOSE: For the development of computer-assisted detection (CAD) software using voxel-based classification, gold standards defined by pixel-by-pixel painting, called painted gold standards, are desirable. However, for radiologists who define gold standards, a simplified method of definition is desirable. One of the simplest methods of defining gold standards is a spherical region, called a spherical gold standard. In this study, we investigated whether spherical gold standards can be used as an alternative to painted gold standards for computerized detection using voxel-based classification.
MATERIALS AND METHODS: The spherical gold standards were determined by the center of gravity and the maximum diameter. We compared two types of gold standard, painted gold standards and spherical gold standards, by two types of CAD software using voxel-based classification.
RESULTS: The time required to paint the area of one lesion was 4.7-6.5 times longer than the time required to define a spherical gold standard. For the same performance of the CAD software, the number of training cases required for the spherical gold standard was 1.6-7.6 times that for the painted gold standards.
CONCLUSION: Spherical gold standards can be used as an alternative to painted gold standards for the computerized detection of lesions with simple shapes.

Keywords:  Computer-assisted detection; Gold standard; Voxel-based classification

Mesh:

Year:  2018        PMID: 30343401     DOI: 10.1007/s11604-018-0784-6

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN: 1867-1071            Impact factor:   2.374


  14 in total

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Authors:  Bram van Ginneken; Samuel G Armato; Bartjan de Hoop; Saskia van Amelsvoort-van de Vorst; Thomas Duindam; Meindert Niemeijer; Keelin Murphy; Arnold Schilham; Alessandra Retico; Maria Evelina Fantacci; Niccolò Camarlinghi; Francesco Bagagli; Ilaria Gori; Takeshi Hara; Hiroshi Fujita; Gianfranco Gargano; Roberto Bellotti; Sabina Tangaro; Lourdes Bolaños; Francesco De Carlo; Piergiorgio Cerello; Sorin Cristian Cheran; Ernesto Lopez Torres; Mathias Prokop
Journal:  Med Image Anal       Date:  2010-06-04       Impact factor: 8.545

2.  3-D segmentation algorithm of small lung nodules in spiral CT images.

Authors:  S Diciotti; G Picozzi; M Falchini; M Mascalchi; N Villari; G Valli
Journal:  IEEE Trans Inf Technol Biomed       Date:  2008-01

3.  Symmetric region growing.

Authors:  Shu-Yen Wan; William E Higgins
Journal:  IEEE Trans Image Process       Date:  2003       Impact factor: 10.856

4.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans.

Authors:  Qiang Li; Shusuke Sone; Kunio Doi
Journal:  Med Phys       Date:  2003-08       Impact factor: 4.071

5.  A computational approach to edge detection.

Authors:  J Canny
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1986-06       Impact factor: 6.226

6.  An automatic method to discriminate malignant masses from normal tissue in digital mammograms.

Authors:  G M te Brake; N Karssemeijer; J H Hendriks
Journal:  Phys Med Biol       Date:  2000-10       Impact factor: 3.609

7.  Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans.

Authors:  Jan-Martin Kuhnigk; Volker Dicken; Lars Bornemann; Annemarie Bakai; Dag Wormanns; Stefan Krass; Heinz-Otto Peitgen
Journal:  IEEE Trans Med Imaging       Date:  2006-04       Impact factor: 10.048

8.  Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans.

Authors:  B C Lassen; C Jacobs; J-M Kuhnigk; B van Ginneken; E M van Rikxoort
Journal:  Phys Med Biol       Date:  2015-01-16       Impact factor: 3.609

9.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

Authors:  Samuel G Armato; Geoffrey McLennan; Luc Bidaut; Michael F McNitt-Gray; Charles R Meyer; Anthony P Reeves; Binsheng Zhao; Denise R Aberle; Claudia I Henschke; Eric A Hoffman; Ella A Kazerooni; Heber MacMahon; Edwin J R Van Beeke; David Yankelevitz; Alberto M Biancardi; Peyton H Bland; Matthew S Brown; Roger M Engelmann; Gary E Laderach; Daniel Max; Richard C Pais; David P Y Qing; Rachael Y Roberts; Amanda R Smith; Adam Starkey; Poonam Batrah; Philip Caligiuri; Ali Farooqi; Gregory W Gladish; C Matilda Jude; Reginald F Munden; Iva Petkovska; Leslie E Quint; Lawrence H Schwartz; Baskaran Sundaram; Lori E Dodd; Charles Fenimore; David Gur; Nicholas Petrick; John Freymann; Justin Kirby; Brian Hughes; Alessi Vande Casteele; Sangeeta Gupte; Maha Sallamm; Michael D Heath; Michael H Kuhn; Ekta Dharaiya; Richard Burns; David S Fryd; Marcos Salganicoff; Vikram Anand; Uri Shreter; Stephen Vastagh; Barbara Y Croft
Journal:  Med Phys       Date:  2011-02       Impact factor: 4.071

10.  A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).

Authors:  Kenji Suzuki
Journal:  Phys Med Biol       Date:  2009-08-18       Impact factor: 3.609

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  2 in total

1.  Anomaly detection in chest 18F-FDG PET/CT by Bayesian deep learning.

Authors:  Takahiro Nakao; Shouhei Hanaoka; Yukihiro Nomura; Naoto Hayashi; Osamu Abe
Journal:  Jpn J Radiol       Date:  2022-01-30       Impact factor: 2.701

2.  Novel platform for development, training, and validation of computer-assisted detection/diagnosis software.

Authors:  Yukihiro Nomura; Soichiro Miki; Naoto Hayashi; Shouhei Hanaoka; Issei Sato; Takeharu Yoshikawa; Yoshitaka Masutani; Osamu Abe
Journal:  Int J Comput Assist Radiol Surg       Date:  2020-03-09       Impact factor: 2.924

  2 in total

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