A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

@article{Farag2017ABA,
  title={A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling},
  author={Amal A. Farag and Le Lu and Holger R. Roth and Jiamin Liu and Evrim B Turkbey and Ronald M. Summers},
  journal={IEEE Transactions on Image Processing},
  year={2017},
  volume={26},
  pages={386-399}
}
Robust organ segmentation is a prerequisite for computer-aided diagnosis, quantitative imaging analysis, pathology detection, and surgical assistance. For organs with high anatomical variability (e.g., the pancreas), previous segmentation approaches report low accuracies, compared with well-studied organs, such as the liver or heart. We present an automated bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method generates a hierarchical cascade of… 

Automatic Pancreas Segmentation via Coarse Location and Ensemble Learning

TLDR
A two-stage, ensemble-based fully convolutional neural network (FCN) to solve the challenging pancreas segmentation problem in CT images and Experimental results show its superior performance compared with several state-of-the-art methods.

Hierarchical Framework for Automatic Pancreas Segmentation in MRI Using Continuous Max-Flow and Min-Cuts Approach

TLDR
A framework that employs a hierarchical pooling of information as follows is proposed, to identify major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue and eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, curvature and position between distinct contours.

Automatic Pancreas Segmentation in CT Images With Distance-Based Saliency-Aware DenseASPP Network

TLDR
This paper proposes a novel distance-based saliency-aware model, namely DSD-ASPP-Net, to fully use coarse segmentation to highlight the pancreas feature and boost accuracy in the fine segmentation stage, outperforming former coarse-to-fine methods.

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks

TLDR
Experimental results show that the proposed automatic organ segmentation framework can precisely and efficiently detect the organs and demonstrates that it is a favorable method for lung segmentation of HRCT scans.

Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks

TLDR
This work proposed a fully automated two stage framework for pancreas segmentation based on convolutional neural networks (CNN), and evaluated the performance of the proposed method on the NIH computed tomography dataset, and verified its superiority over other state-of-the-art 2D and 3D approaches for pancakes segmentation in terms of dice-sorensen coefficient (DSC) accuracy in testing.

Pancreas segmentation using a dual-input v-mesh network

Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks

TLDR
This paper formulate pancreas segmentation in magnetic resonance imaging (MRI) scans as a graph based decision fusion process combined with deep convolutional neural networks (CNN) to achieve the best results compared with other state of the arts.

Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning

TLDR
Recurrent neural networks (RNNs) are introduced to address the problem of spatial non-smoothness of inter-slice pancreas segmentation across adjacent image slices and regularizes the segmentation of an image by integrating predictions of its neighboring slices.

Pancreas Segmentation in Abdominal CT Scans using Inter-/Intra-Slice Contextual Information with a Cascade Neural Network

  • Zhengzheng YangLei Zhang Yi Lv
  • Computer Science, Medicine
    2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2019
TLDR
A new approach for automatic pancreas segmentation of CT images using inter-/intra-slice contextual information with a cascade neural network is proposed and outperforms the state-of-the-arts with an average Dice Similarity Coefficient of 87.72 for NIH dataset with 4-fold cross-validation.
...

References

SHOWING 1-10 OF 82 REFERENCES

A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans

TLDR
A fully-automated bottom-up method is presented for pancreas segmentation, using abdominal computed tomography (CT) scans, based on a hierarchical two-tiered information propagation by classifying image patches.

Deep convolutional networks for pancreas segmentation in CT imaging

TLDR
This work presents a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen based on hierarchical coarse-to-fine classification of local image regions (superpixels).

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

TLDR
This paper presents a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography CT scans, using multi-level deep convolutional networks ConvNets, and proposes and evaluates several variations of deep ConvNETS in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels.

Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation

TLDR
A general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlasRegistration and patch-based segmentation, two widely used methods in brain segmentation.

Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

TLDR
This paper presents a principled three-tiered image feature learning approach to capture task specific and data-driven class discriminative statistics from an annotated image database that integrates voxel-, instance-, and database-level feature learning, aggregation and parsing.

Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

TLDR
This work combines and refining state-of-the-art techniques (random forests and template deformation) to demonstrate the possibility of building an algorithm that meets practical use in clinical routine and is fast, automatic and robust to contrast-agent enhancement and fields of view.

Hierarchical, learning-based automatic liver segmentation

TLDR
This paper presents a hierarchical, learning-based approach for automatic and accurate liver segmentation from 3D CT volumes that come from largely diverse sources and are generated by different scanning protocols.

Hierarchical Multi-Organ Segmentation Without Registration in 3D Abdominal CT Images

We present a novel framework for the segmentation of multiple organs in 3D abdominal CT images, which does not require registration with an atlas. Instead we use discriminative classifiers that have

Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

TLDR
This study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the Pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.

Effective 3D object detection and regression using probabilistic segmentation features in CT images

TLDR
An effective image segmentation features and a novel multiple instance regression method for solving the above challenges and shows excellent performance on effectively classifying ambiguous positive and negative VOIs, for the CAD system of detecting colonic polyps using CT images.
...