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Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks
Spleen volume estimation using automated image segmentation technique ma...
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Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
Whole brain segmentation on structural magnetic resonance imaging (MRI) ...
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Multi-modal segmentation of 3D brain scans using neural networks
Purpose: To implement a brain segmentation pipeline based on convolution...
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PIMMS: Permutation Invariant Multi-Modal Segmentation
In a research context, image acquisition will often involve a pre-define...
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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...
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M^2S-Net: Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection
Recent works using artificial neural networks based on distributed word ...
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Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
Accurate and robust segmentation of small organs in whole-body MRI is di...
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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 non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to (1) large anatomical and spatial variations of splenomegaly, (2) large inter- and intra-scan intensity variations on multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce the deep convolutional neural network (DCNN) approaches in multi-modal MRI splenomegaly segmentation. Large convolutional kernel layers were used to address the spatial and anatomical variations, while the conditional generative adversarial networks (GAN) were employed to leverage the segmentation performance of SS-Net in an end-to-end manner. A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network. From the experimental results, the DCNN methods achieved superior performance to the state-of-the-art MAS method. The proposed SS-Net method achieved the highest median and mean Dice scores among investigated baseline DCNN methods.
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