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3-D Surface Segmentation Meets Conditional Random Fields
Automated surface segmentation is important and challenging in many medi...
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Segmentation of histological images and fibrosis identification with a convolutional neural network
Segmentation of histological images is one of the most crucial tasks for...
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Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors
Automated surface segmentation is important and challenging in many medi...
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CNN-Based Deep Architecture for Reinforced Concrete Delamination Segmentation Through Thermography
Delamination assessment of the bridge deck plays a vital role for bridge...
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Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
In this work, we propose to resolve the issue existing in current deep l...
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Elastic Boundary Projection for 3D Medical Imaging Segmentation
We focus on an important yet challenging problem: using a 2D deep networ...
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MedMeshCNN – Enabling MeshCNN for Medical Surface Models
Background and objective: MeshCNN is a recently proposed Deep Learning f...
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End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture – organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.
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