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When Does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?

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When Does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification? / Baltruschat, Ivo Matteo; Steinmeister, Leonhard; Ittrich, Harald; Adam, Gerhard; Nickisch, Hannes; Saalbach, Axel; von Berg, J; Grass, Michael; Knopp, Tobias.

IEEE: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) . 2019. p. 1362-1366.

Research output: SCORING: Contribution to book/anthology › Conference contribution - Article for conference › Research › peer-review

Harvard

Baltruschat, IM, Steinmeister, L, Ittrich, H, Adam, G, Nickisch, H, Saalbach, A, von Berg, J, Grass, M & Knopp, T 2019, When Does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification? in IEEE: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) . pp. 1362-1366.

APA

Baltruschat, I. M., Steinmeister, L., Ittrich, H., Adam, G., Nickisch, H., Saalbach, A., von Berg, J., Grass, M., & Knopp, T. (2019). When Does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification? In IEEE: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 1362-1366)

Vancouver

Baltruschat IM, Steinmeister L, Ittrich H, Adam G, Nickisch H, Saalbach A et al. When Does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification? In IEEE: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) . 2019. p. 1362-1366

Bibtex

@inbook{e96ac9ea497545378e4a79a832bf0b55,
title = "When Does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?",
abstract = "Chest radiography is the most common clinical examination type. To improve the quality of patient care and to reduce workload, methods for automatic pathology classificationhave been developed. In this contribution we investigate theusefulness of two advanced image pre-processing techniques,initially developed for image reading by radiologists, for theperformance of Deep Learning methods. First, we use bonesuppression, an algorithm to artificially remove the rib cage. Secondly, we employ an automatic lung field detection to crop the image to the lung area. Furthermore, we consider the combination of both in the context of an ensemble approach. In a five-times re-sampling scheme, we use Receiver Op-erating Characteristic (ROC) statistics to evaluate the effect ofthe pre-processing approaches. Using a Convolutional NeuralNetwork (CNN), optimized for X-ray analysis, we achieve a good performance with respect to all pathologies on average. Superior results are obtained for selected pathologies when using pre-processing, i.e. for mass the area under the ROC curve increased by 9.95%. The ensemble with pre-processed trained models yields the best overall results.",
author = "Baltruschat, {Ivo Matteo} and Leonhard Steinmeister and Harald Ittrich and Gerhard Adam and Hannes Nickisch and Axel Saalbach and {von Berg}, J and Michael Grass and Tobias Knopp",
year = "2019",
language = "English",
pages = "1362--1366",
booktitle = "IEEE",

}

RIS

TY - CHAP

T1 - When Does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?

AU - Baltruschat, Ivo Matteo

AU - Steinmeister, Leonhard

AU - Ittrich, Harald

AU - Adam, Gerhard

AU - Nickisch, Hannes

AU - Saalbach, Axel

AU - von Berg, J

AU - Grass, Michael

AU - Knopp, Tobias

PY - 2019

Y1 - 2019

N2 - Chest radiography is the most common clinical examination type. To improve the quality of patient care and to reduce workload, methods for automatic pathology classificationhave been developed. In this contribution we investigate theusefulness of two advanced image pre-processing techniques,initially developed for image reading by radiologists, for theperformance of Deep Learning methods. First, we use bonesuppression, an algorithm to artificially remove the rib cage. Secondly, we employ an automatic lung field detection to crop the image to the lung area. Furthermore, we consider the combination of both in the context of an ensemble approach. In a five-times re-sampling scheme, we use Receiver Op-erating Characteristic (ROC) statistics to evaluate the effect ofthe pre-processing approaches. Using a Convolutional NeuralNetwork (CNN), optimized for X-ray analysis, we achieve a good performance with respect to all pathologies on average. Superior results are obtained for selected pathologies when using pre-processing, i.e. for mass the area under the ROC curve increased by 9.95%. The ensemble with pre-processed trained models yields the best overall results.

AB - Chest radiography is the most common clinical examination type. To improve the quality of patient care and to reduce workload, methods for automatic pathology classificationhave been developed. In this contribution we investigate theusefulness of two advanced image pre-processing techniques,initially developed for image reading by radiologists, for theperformance of Deep Learning methods. First, we use bonesuppression, an algorithm to artificially remove the rib cage. Secondly, we employ an automatic lung field detection to crop the image to the lung area. Furthermore, we consider the combination of both in the context of an ensemble approach. In a five-times re-sampling scheme, we use Receiver Op-erating Characteristic (ROC) statistics to evaluate the effect ofthe pre-processing approaches. Using a Convolutional NeuralNetwork (CNN), optimized for X-ray analysis, we achieve a good performance with respect to all pathologies on average. Superior results are obtained for selected pathologies when using pre-processing, i.e. for mass the area under the ROC curve increased by 9.95%. The ensemble with pre-processed trained models yields the best overall results.

M3 - Conference contribution - Article for conference

SP - 1362

EP - 1366

BT - IEEE

ER -