Automated breast mass detection in 3D reconstructed tomosynthesis volumes: A featureless approach

The purpose of this study was to propose and implement a computer aided detection (CADe) tool for breast tomosynthesis. This task was accomplished in two stages—a highly sensitive mass detector followed by a false positive (FP) reduction stage. Breast tomosynthesis data from 100 human subject cases...

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Vydané v:	Medical physics (Lancaster) Ročník 35; číslo 8; s. 3626 - 3636
Hlavní autori:	Singh, Swatee, Tourassi, Georgia D., Baker, Jay A., Samei, Ehsan, Lo, Joseph Y.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States American Association of Physicists in Medicine 01.08.2008
Predmet:	Algorithms biological organs Breast - pathology breast imaging Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology Computed tomography computer aided detection Computer aided diagnosis computerised tomography Digital tomosynthesis mammography False Positive Reactions Female gynaecology Humans image reconstruction Image sensors Information and communication theory information theory knowledge base Knowledge bases mammography Mammography - methods mass detection masses medical image processing Medical imaging mutual information Pattern Recognition, Automated - methods projection images Radiation Imaging Physics Radiographic Image Interpretation, Computer-Assisted - methods Radiologists reconstructed volume Reconstruction Sensitivity and Specificity tomosynthesis knowledge base mammography tomosynthesis reconstructed volume computer aided detection mass detection projection images information theory mutual information breast imaging masses
ISSN:	0094-2405, 2473-4209, 0094-2405
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Abstract	The purpose of this study was to propose and implement a computer aided detection (CADe) tool for breast tomosynthesis. This task was accomplished in two stages—a highly sensitive mass detector followed by a false positive (FP) reduction stage. Breast tomosynthesis data from 100 human subject cases were used, of which 25 subjects had one or more mass lesions and the rest were normal. For stage 1, filter parameters were optimized via a grid search. The CADe identified suspicious locations were reconstructed to yield 3D CADe volumes of interest. The first stage yielded a maximum sensitivity of 93% with 7.7 FPs/breast volume. Unlike traditional CADe algorithms in which the second stage FP reduction is done via feature extraction and analysis, instead information theory principles were used with mutual information as a similarity metric. Three schemes were proposed, all using leave-one-case-out cross validation sampling. The three schemes, A, B, and C, differed in the composition of their knowledge base of regions of interest (ROIs). Scheme A’s knowledge base was comprised of all the mass and FP ROIs generated by the first stage of the algorithm. Scheme B had a knowledge base that contained information from mass ROIs and randomly extracted normal ROIs. Scheme C had information from three sources of information—masses, FPs, and normal ROIs. Also, performance was assessed as a function of the composition of the knowledge base in terms of the number of FP or normal ROIs needed by the system to reach optimal performance. The results indicated that the knowledge base needed no more than 20 times as many FPs and 30 times as many normal ROIs as masses to attain maximal performance. The best overall system performance was 85% sensitivity with 2.4 FPs per breast volume for scheme A, 3.6 FPs per breast volume for scheme B, and 3 FPs per breast volume for scheme C.
AbstractList	The purpose of this study was to propose and implement a computer aided detection (CADe) tool for breast tomosynthesis. This task was accomplished in two stages-a highly sensitive mass detector followed by a false positive (FP) reduction stage. Breast tomosynthesis data from 100 human subject cases were used, of which 25 subjects had one or more mass lesions and the rest were normal. For stage 1, filter parameters were optimized via a grid search. The CADe identified suspicious locations were reconstructed to yield 3D CADe volumes of interest. The first stage yielded a maximum sensitivity of 93% with 7.7 FPs/breast volume. Unlike traditional CADe algorithms in which the second stage FP reduction is done via feature extraction and analysis, instead information theory principles were used with mutual information as a similarity metric. Three schemes were proposed, all using leave-one-case-out cross validation sampling. The three schemes, A, B, and C, differed in the composition of their knowledge base of regions of interest (ROIs). Scheme A's knowledge base was comprised of all the mass and FP ROIs generated by the first stage of the algorithm. Scheme B had a knowledge base that contained information from mass ROIs and randomly extracted normal ROIs. Scheme C had information from three sources of information-masses, FPs, and normal ROIs. Also, performance was assessed as a function of the composition of the knowledge base in terms of the number of FP or normal ROIs needed by the system to reach optimal performance. The results indicated that the knowledge base needed no more than 20 times as many FPs and 30 times as many normal ROIs as masses to attain maximal performance. The best overall system performance was 85% sensitivity with 2.4 FPs per breast volume for scheme A, 3.6 FPs per breast volume for scheme B, and 3 FPs per breast volume for scheme C. The purpose of this study was to propose and implement a computer aided detection (CADe) tool for breast tomosynthesis. This task was accomplished in two stages-a highly sensitive mass detector followed by a false positive (FP) reduction stage. Breast tomosynthesis data from 100 human subject cases were used, of which 25 subjects had one or more mass lesions and the rest were normal. For stage 1, filter parameters were optimized via a grid search. The CADe identified suspicious locations were reconstructed to yield 3D CADe volumes of interest. The first stage yielded a maximum sensitivity of 93% with 7.7 FPs/breast volume. Unlike traditional CADe algorithms in which the second stage FP reduction is done via feature extraction and analysis, instead information theory principles were used with mutual information as a similarity metric. Three schemes were proposed, all using leave-one-case-out cross validation sampling. The three schemes, A, B, and C, differed in the composition of their knowledge base of regions of interest (ROIs). Scheme A's knowledge base was comprised of all the mass and FP ROIs generated by the first stage of the algorithm. Scheme B had a knowledge base that contained information from mass ROIs and randomly extracted normal ROIs. Scheme C had information from three sources of information-masses, FPs, and normal ROIs. Also, performance was assessed as a function of the composition of the knowledge base in terms of the number of FP or normal ROIs needed by the system to reach optimal performance. The results indicated that the knowledge base needed no more than 20 times as many FPs and 30 times as many normal ROIs as masses to attain maximal performance. The best overall system performance was 85% sensitivity with 2.4 FPs per breast volume for scheme A, 3.6 FPs per breast volume for scheme B, and 3 FPs per breast volume for scheme C.The purpose of this study was to propose and implement a computer aided detection (CADe) tool for breast tomosynthesis. This task was accomplished in two stages-a highly sensitive mass detector followed by a false positive (FP) reduction stage. Breast tomosynthesis data from 100 human subject cases were used, of which 25 subjects had one or more mass lesions and the rest were normal. For stage 1, filter parameters were optimized via a grid search. The CADe identified suspicious locations were reconstructed to yield 3D CADe volumes of interest. The first stage yielded a maximum sensitivity of 93% with 7.7 FPs/breast volume. Unlike traditional CADe algorithms in which the second stage FP reduction is done via feature extraction and analysis, instead information theory principles were used with mutual information as a similarity metric. Three schemes were proposed, all using leave-one-case-out cross validation sampling. The three schemes, A, B, and C, differed in the composition of their knowledge base of regions of interest (ROIs). Scheme A's knowledge base was comprised of all the mass and FP ROIs generated by the first stage of the algorithm. Scheme B had a knowledge base that contained information from mass ROIs and randomly extracted normal ROIs. Scheme C had information from three sources of information-masses, FPs, and normal ROIs. Also, performance was assessed as a function of the composition of the knowledge base in terms of the number of FP or normal ROIs needed by the system to reach optimal performance. The results indicated that the knowledge base needed no more than 20 times as many FPs and 30 times as many normal ROIs as masses to attain maximal performance. The best overall system performance was 85% sensitivity with 2.4 FPs per breast volume for scheme A, 3.6 FPs per breast volume for scheme B, and 3 FPs per breast volume for scheme C. The purpose of this study was to propose and implement a computer aided detection (CADe) tool for breast tomosynthesis. This task was accomplished in two stages—a highly sensitive mass detector followed by a false positive (FP) reduction stage. Breast tomosynthesis data from 100 human subject cases were used, of which 25 subjects had one or more mass lesions and the rest were normal. For stage 1, filter parameters were optimized via a grid search. The CADe identified suspicious locations were reconstructed to yield 3D CADe volumes of interest. The first stage yielded a maximum sensitivity of 93% with 7.7 FPs∕breast volume. Unlike traditional CADe algorithms in which the second stage FP reduction is done via feature extraction and analysis, instead information theory principles were used with mutual information as a similarity metric. Three schemes were proposed, all using leave-one-case-out cross validation sampling. The three schemes, A, B, and C, differed in the composition of their knowledge base of regions of interest (ROIs). Scheme A’s knowledge base was comprised of all the mass and FP ROIs generated by the first stage of the algorithm. Scheme B had a knowledge base that contained information from mass ROIs and randomly extracted normal ROIs. Scheme C had information from three sources of information—masses, FPs, and normal ROIs. Also, performance was assessed as a function of the composition of the knowledge base in terms of the number of FP or normal ROIs needed by the system to reach optimal performance. The results indicated that the knowledge base needed no more than 20 times as many FPs and 30 times as many normal ROIs as masses to attain maximal performance. The best overall system performance was 85% sensitivity with 2.4 FPs per breast volume for scheme A, 3.6 FPs per breast volume for scheme B, and 3 FPs per breast volume for scheme C.
Author	Baker, Jay A. Singh, Swatee Lo, Joseph Y. Samei, Ehsan Tourassi, Georgia D.
Author_xml	– sequence: 1 givenname: Swatee surname: Singh fullname: Singh, Swatee organization: Duke Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705 and Department of Biomedical Engineering, Duke University, Durham, North Carolina 27705 – sequence: 2 givenname: Georgia D. surname: Tourassi fullname: Tourassi, Georgia D. organization: Duke Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, and Department of Medical Physics, Duke University, Durham, North Carolina 27705 – sequence: 3 givenname: Jay A. surname: Baker fullname: Baker, Jay A. organization: Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710 – sequence: 4 givenname: Ehsan surname: Samei fullname: Samei, Ehsan organization: Duke Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705 and Department of Biomedical Engineering, Department of Medical Physics, and Department of Physics, Duke University, Durham, North Carolina 27710 – sequence: 5 givenname: Joseph Y. surname: Lo fullname: Lo, Joseph Y. organization: Duke Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705 and Department of Biomedical Engineering and Department of Medical Physics, Duke University, Durham, North Carolina 27705
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Notes	swatee.singh@duke.edu Author to whom correspondence should be addressed. Electronic mail ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author to whom correspondence should be addressed. Electronic mail: swatee.singh@duke.edu
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Snippet	The purpose of this study was to propose and implement a computer aided detection (CADe) tool for breast tomosynthesis. This task was accomplished in two...
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SubjectTerms	Algorithms biological organs Breast - pathology breast imaging Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology Computed tomography computer aided detection Computer aided diagnosis computerised tomography Digital tomosynthesis mammography False Positive Reactions Female gynaecology Humans image reconstruction Image sensors Information and communication theory information theory knowledge base Knowledge bases mammography Mammography - methods mass detection masses medical image processing Medical imaging mutual information Pattern Recognition, Automated - methods projection images Radiation Imaging Physics Radiographic Image Interpretation, Computer-Assisted - methods Radiologists reconstructed volume Reconstruction Sensitivity and Specificity tomosynthesis
Title	Automated breast mass detection in 3D reconstructed tomosynthesis volumes: A featureless approach
URI	http://dx.doi.org/10.1118/1.2953562 https://onlinelibrary.wiley.com/doi/abs/10.1118%2F1.2953562 https://www.ncbi.nlm.nih.gov/pubmed/18777923 https://www.proquest.com/docview/69526362 https://pubmed.ncbi.nlm.nih.gov/PMC2673649
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