Pennig, L., Shahzad, R., Caldeira, L., Lennartz, S., Thiele, F., Goertz, L., Zopfs, D., Meissner, A-K, Fuertjes, G., Perkuhn, M., Kabbasch, C., Grau, S., Borggrefe, J. and Laukamp, K. R. (2021). Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model. Am. J. Neuroradiol., 42 (4). S. 655 - 663. DENVILLE: AMER SOC NEURORADIOLOGY. ISSN 1936-959X
Full text not available from this repository.Abstract
Deep learning-based automated detection and segmentation of brain metastases in malignant melanoma yield high detection and segmentation accuracy with false-positive findings of <1 per scan. BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging. MATERIALS AND METHODS: Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth. RESULTS: After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4]?cm(3)) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75. CONCLUSIONS: Deep learning?based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of <1 per scan.
| Item Type: | Journal Article | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| URN: | urn:nbn:de:hbz:38-592052 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DOI: | 10.3174/ajnr.A6982 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Journal or Publication Title: | Am. J. Neuroradiol. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Volume: | 42 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number: | 4 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Page Range: | S. 655 - 663 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Date: | 2021 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Publisher: | AMER SOC NEURORADIOLOGY | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Place of Publication: | DENVILLE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ISSN: | 1936-959X | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Language: | English | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Faculty: | Unspecified | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Divisions: | Unspecified | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Subjects: | no entry | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| URI: | http://kups.ub.uni-koeln.de/id/eprint/59205 |
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