TY - GEN
T1 - Automatic detection of contrast enhancement in T1-weighted brain MRI of human adults
AU - Milchenko, Mikhail
AU - Lamontagne, Pamela
AU - Marcus, Daniel
N1 - Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - Retrospective neuro-oncology imaging research relies on standardization of large, heterogeneous sets of clinical images. In particular, many tumor segmentation algorithms require pre- and post- Gadolinium (Gd) T1-weighted MRI scans. Since the presence of contrast agent cannot be reliably inferred from image metadata, we propose an automatic imagebased classifier for this purpose. We proceed with aligning a T1-weighted MR image to a standard atlas space, by selecting one of eight affine transforms produced using different registration parameters and atlases. After resampling to the standard space, we normalize the intensity distribution and compute intensity characteristics inside a pre-built binary mask of likely enhancement. Using a labeled set of 1892 scans, we evaluated logistic regressions with mean, standard deviation, and 95th percentile as possible factors. A univariable logistic regression with standard deviation as factor was most accurate at 98.9% on testing data. The slope coefficient was highly robust with p<1e-6 and Cramer-Rao bound on variance of 1%. The resulting classification script is completely unsupervised. Accuracy on two validation datasets from different sources (totaling 1328 scans) was over 99% on scans with isotropic sampling. Accuracy was lower on highly anisotropic or otherwise lower quality scans. To our knowledge, this is the first attempt to build an automated Gd enhancement classifier for big data applications. We plan to integrate it into XNAT platform for automatic labeling, to enable Gd enhanced image search. The proposed detector performed well on a wide variety of acquisition parameters. Image anisotropy and acquisition artifacts may interfere with accurate detection.
AB - Retrospective neuro-oncology imaging research relies on standardization of large, heterogeneous sets of clinical images. In particular, many tumor segmentation algorithms require pre- and post- Gadolinium (Gd) T1-weighted MRI scans. Since the presence of contrast agent cannot be reliably inferred from image metadata, we propose an automatic imagebased classifier for this purpose. We proceed with aligning a T1-weighted MR image to a standard atlas space, by selecting one of eight affine transforms produced using different registration parameters and atlases. After resampling to the standard space, we normalize the intensity distribution and compute intensity characteristics inside a pre-built binary mask of likely enhancement. Using a labeled set of 1892 scans, we evaluated logistic regressions with mean, standard deviation, and 95th percentile as possible factors. A univariable logistic regression with standard deviation as factor was most accurate at 98.9% on testing data. The slope coefficient was highly robust with p<1e-6 and Cramer-Rao bound on variance of 1%. The resulting classification script is completely unsupervised. Accuracy on two validation datasets from different sources (totaling 1328 scans) was over 99% on scans with isotropic sampling. Accuracy was lower on highly anisotropic or otherwise lower quality scans. To our knowledge, this is the first attempt to build an automated Gd enhancement classifier for big data applications. We plan to integrate it into XNAT platform for automatic labeling, to enable Gd enhanced image search. The proposed detector performed well on a wide variety of acquisition parameters. Image anisotropy and acquisition artifacts may interfere with accurate detection.
KW - automatic labeling
KW - contrast detetection
KW - neuro-oncology mri
KW - spatial normalization
UR - http://www.scopus.com/inward/record.url?scp=85085519278&partnerID=8YFLogxK
U2 - 10.1117/12.2547528
DO - 10.1117/12.2547528
M3 - Conference contribution
AN - SCOPUS:85085519278
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Hahn, Horst K.
A2 - Mazurowski, Maciej A.
PB - SPIE
T2 - Medical Imaging 2020: Computer-Aided Diagnosis
Y2 - 16 February 2020 through 19 February 2020
ER -