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• W3101381711 abstract "Primary tumors have a high likelihood of developing metastases in the liver, and early detection of these metastases is crucial for patient outcome. We propose a method based on convolutional neural networks to detect liver metastases. First, the liver is automatically segmented using the six phases of abdominal dynamic contrast-enhanced (DCE) MR images. Next, DCE-MR and diffusion weighted MR images are used for metastases detection within the liver mask. The liver segmentations have a median Dice similarity coefficient of 0.95 compared with manual annotations. The metastases detection method has a sensitivity of 99.8% with a median of two false positives per image. The combination of the two MR sequences in a dual pathway network is proven valuable for the detection of liver metastases. In conclusion, a high quality liver segmentation can be obtained in which we can successfully detect liver metastases." @default.
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• W3101381711 date "2019-10-15" @default.
• W3101381711 modified "2023-10-01" @default.
• W3101381711 title "Liver segmentation and metastases detection in MR images using convolutional neural networks" @default.
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• W3101381711 doi "https://doi.org/10.1117/1.jmi.6.4.044003" @default.
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