Effect of massive training artificial neural networks for rib suppression on reduction of false positives in computerized detection of nodules on chest radiographs

Kenji Suzuki; Junji Shiraishi; Feng Li; Hiroyuki Abe; Heber MacMahon; Kunio Doi

doi:10.1117/12.594730

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29 April 2005 Effect of massive training artificial neural networks for rib suppression on reduction of false positives in computerized detection of nodules on chest radiographs

Kenji Suzuki, Junji Shiraishi, Feng Li, Hiroyuki Abe, Heber MacMahon, Kunio Doi

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Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.594730
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

A major challenge in computer-aided diagnostic (CAD) schemes for nodule detection on chest radiographs is the detection of nodules that overlap with ribs. Our purpose was to develop a technique for false-positive reduction in a CAD scheme using a rib-suppression technique based on massive training artificial neural networks (MTANNs). We developed a multiple MTANN (multi-MTANN) consisting of eight MTANNs for removing eight types of false positives. For further removal of false positives caused by ribs, we developed a rib-suppression technique using a multi-resolution MTANN consisting of three different resolution MTANNs. To suppress the contrast of ribs, the multi-resolution MTANN was trained with input chest radiographs and the teaching soft-tissue images obtained by using a dual-energy subtraction technique. Our database consisted of 91 nodules in 91 chest radiographs. With our original CAD scheme based on a difference image technique with linear discriminant analysis, a sensitivity of 82.4% (75/91 nodules) with 4.5 (410/91) false positives per image was achieved. The trained multi-MTANN was able to remove 62.7% (257/410) of false positives with a loss of one true positive. With the rib-suppression technique, the contrast of ribs in chest radiographs was suppressed substantially. Due to the effect of rib-suppression, 41.2% (63/153) of the remaining false positives were removed without a loss of any true positives. Thus, the false-positive rate of our CAD scheme was improved substantially, while a high sensitivity was maintained.

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Kenji Suzuki, Junji Shiraishi, Feng Li, Hiroyuki Abe, Heber MacMahon, and Kunio Doi "Effect of massive training artificial neural networks for rib suppression on reduction of false positives in computerized detection of nodules on chest radiographs", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.594730

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