&lt;title&gt;Computer-aided detection of lung cancer on chest radiographs: algorithm performance vs. radiologists' performance by size of cancer&lt;/title&gt;

Freedman, Matthew T.; Lo, Shih-Chung Benedict; Lure, Fleming; Xu, Xinwei; Lin, Jesse; Zhao, Hui; Osicka, Teresa; Ron, Zhang

doi:10.1117/12.428048

Cited by 10 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common digital image database is useful for evaluating the performances among the different CAD systems. Although we achieved a detection sensitivity of 73% with an average of 4.0 false-positive detections per image in this study, our results cannot be compared directly with those obtained in other previous reports because of the differences in the database [9][10][11]. Additional studies seem to be needed to further validate the performance of our CAD system, comparing it with other systems.…”

Section: Discussioncontrasting

confidence: 54%

“…However, the CAD systems used in previous studies were laboratory products. On July 2001, the United States Food and Drug Administration (FDA) approved RapidScreen RS-2000 (DEUS Technologies, Rockville, MD) for marketing as a device that assists in the detection of early-stage lung cancer [9][10][11]. This approval marked the first time that the FDA sanctioned the marketing of a device specifically targeted for the detection of lung nodules.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Improved Detection of Lung Nodules on Chest Radiographs Using a Commercial Computer-Aided Diagnosis System

Kakeda

Moriya

Sato

et al. 2004

American Journal of Roentgenology

127

View full text Add to dashboard Cite

show abstract

Section: Discussioncontrasting

confidence: 54%

mentioning

confidence: 99%

Improved Detection of Lung Nodules on Chest Radiographs Using a Commercial Computer-Aided Diagnosis System

Kakeda

Moriya

Sato

et al. 2004

American Journal of Roentgenology

127

View full text Add to dashboard Cite

show abstract

“…As we have previously reported [1], each radiologist identifies a somewhat different group of cancers without using CAD. A CAD system identifies a specific group of cancers in each database that we can expect will overlap each radiologist's unaided detections to different degrees and on different cases; this will result in differences in the potential benefit available to each radiologist when using CAD (Chart 1).…”

Section: Discussionmentioning

confidence: 91%

The effect of database content on machine performance and radiologist performance improvement with chest CAD

Freedman

Osicka

et al. 2005

International Congress Series

View full text Add to dashboard Cite

“…The first commercial system for lung nodule detection CAD was developed by Deus Technology Inc. ͑Riverain Medical at present͒ and received FDA approval for clinical use in 2001. With this commercial CAD system, Freedman et al 22 reported a sensitivity of 65.0% with 5.3 false positives per image from a clinical trial in an independent validation test by use of 80 cases containing small primary lung cancers and 160 cases not containing cancers. Kakeda et al 23 reported the use of another commercial CAD system developed by Mitsubishi Space Software for the detection of lung nodules: the performance of this CAD system indicated a sensitivity of 73.0% with 4.0 false positives per image for 274 chest radiographs including 323 lung nodules.…”

Section: Introductionmentioning

confidence: 99%

Computer‐aided diagnostic scheme for the detection of lung nodules on chest radiographs: Localized search method based on anatomical classification

Shiraishi

Suzuki

et al. 2006

Medical Physics

View full text Add to dashboard Cite

We developed an advanced computer-aided diagnostic (CAD) scheme for the detection of various types of lung nodules on chest radiographs intended for implementation in clinical situations. We used 924 digitized chest images (992 noncalcified nodules) which had a 500 x 500 matrix size with a 1024 gray scale. The images were divided randomly into two sets which were used for training and testing of the computerized scheme. In this scheme, the lung field was first segmented by use of a ribcage detection technique, and then a large search area (448 x 448 matrix size) within the chest image was automatically determined by taking into account the locations of a midline and a top edge of the segmented ribcage. In order to detect lung nodule candidates based on a localized search method, we divided the entire search area into 7 x 7 regions of interest (ROIs: 64 x 64 matrix size). In the next step, each ROI was classified anatomically into apical, peripheral, hilar, and diaphragm/heart regions by use of its image features. Identification of lung nodule candidates and extraction of image features were applied for each localized region (128 x 128 matrix size), each having its central part (64 x 64 matrix size) located at a position corresponding to a ROI that was classified anatomically in the previous step. Initial candidates were identified by use of the nodule-enhanced image obtained with the average radial-gradient filtering technique, in which the filter size was varied adaptively depending on the location and the anatomical classification of the ROI. We extracted 57 image features from the original and nodule-enhanced images based on geometric, gray-level, background structure, and edge-gradient features. In addition, 14 image features were obtained from the corresponding locations in the contralateral subtraction image. A total of 71 image features were employed for three sequential artificial neural networks (ANNs) in order to reduce the number of false-positive candidates. All parameters for ANNs, i.e., the number of iterations, slope of sigmoid functions, learning rate, and threshold values for removing the false positives, were determined automatically by use of a bootstrap technique with training cases. We employed four different combinations of training and test image data sets which was selected randomly from the 924 cases. By use of our localized search method based on anatomical classification, the average sensitivity was increased to 92.5% with 59.3 false positives per image at the level of initial detection for four different sets of test cases, whereas our previous technique achieved an 82.8% of sensitivity with 56.8 false positives per image. The computer performance in the final step obtained from four different data sets indicated that the average sensitivity in detecting lung nodules was 70.1% with 5.0 false positives per image for testing cases and 70.4% sensitivity with 4.2 false positives per image for training cases. The advanced CAD scheme involving the localized search method with anatomical clas...

show abstract

<title>Computer-aided detection of lung cancer on chest radiographs: algorithm performance vs. radiologists' performance by size of cancer</title>

Cited by 10 publications

References 0 publications

Improved Detection of Lung Nodules on Chest Radiographs Using a Commercial Computer-Aided Diagnosis System

Improved Detection of Lung Nodules on Chest Radiographs Using a Commercial Computer-Aided Diagnosis System

The effect of database content on machine performance and radiologist performance improvement with chest CAD

Computer‐aided diagnostic scheme for the detection of lung nodules on chest radiographs: Localized search method based on anatomical classification

Contact Info

Product

Resources

About