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Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images
Objective: To develop an automatic image normalization algorithm for int...
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Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection
Our works experimented DeepLabV3+ with different backbones on a large vo...
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Automatic Cerebral Vessel Extraction in TOF-MRA Using Deep Learning
Deep learning approaches may help radiologists in the early diagnosis an...
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Deep learning achieves radiologist-level performance of tumor segmentation in breast MRI
Purpose: The goal of this research was to develop a deep network archite...
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Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks
The findings of splenomegaly, abnormal enlargement of the spleen, is a n...
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The Ladder Algorithm: Finding Repetitive Structures in Medical Images by Induction
In this paper we introduce the Ladder Algorithm; a novel recurrent algor...
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Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks
Planning of radiotherapy involves accurate segmentation of a large numbe...
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Intensity augmentation for domain transfer of whole breast segmentation in MRI
The segmentation of the breast from the chest wall is an important first step in the analysis of breast magnetic resonance images. 3D U-nets have been shown to obtain high segmentation accuracy and appear to generalize well when trained on one scanner type and tested on another scanner, provided that a very similar T1-weighted MR protocol is used. There has, however, been little work addressing the problem of domain adaptation when image intensities or patient orientation differ markedly between the training set and an unseen test set. To overcome the domain shift we propose to apply extensive intensity augmentation in addition to geometric augmentation during training. We explored both style transfer and a novel intensity remapping approach as intensity augmentation strategies. For our experiments, we trained a 3D U-net on T1-weighted scans and tested on T2-weighted scans. By applying intensity augmentation we increased segmentation performance from a DSC of 0.71 to 0.90. This performance is very close to the baseline performance of training and testing on T2-weighted scans (0.92). Furthermore, we applied our network to an independent test set made up of publicly available scans acquired using a T1-weighted TWIST sequence and a different coil configuration. On this dataset we obtained a performance of 0.89, close to the inter-observer variability of the ground truth segmentations (0.92). Our results show that using intensity augmentation in addition to geometric augmentation is a suitable method to overcome the intensity domain shift and we expect it to be useful for a wide range of segmentation tasks.
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