medical-6

Pancreas Segmentation

3260 papers • 126 benchmarks • 313 datasets

Pancreas segmentation is the task of segmenting out the pancreas from medical imaging. Convolutional neural network

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in pancreas-segmentation-19

Trend

Dataset

Best Model

Actions

TCIA Pancreas-CT Dataset

CT-150

Libraries

i

Use these libraries to find pancreas-segmentation-19 models and implementations

PaddlePaddle/PaddleSeg

2 papers 7,792

Datasets

StepGame

Subtasks

No subtasks available.

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

O. Ronneberger, T. Brox, P. Fischer•Sun May 17 2015

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

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rezazad68/BCDU-Net

2 papers 632

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Attention U-Net: Learning Where to Look for the Pancreas

D. Rueckert, Nils Y. Hammerla, Ben Glocker, M. Heinrich, O. Oktay, Jo Schlemper, Steven G. McDonagh, Loic Le Folgoc, M. J. Lee, K. Misawa, K. Mori, Bernhard Kainz•Tue Apr 10 2018

A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).

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A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

A. Yuille, Wei Shen, Yan Wang, Lingxi Xie, Yuyin Zhou, E. Fishman•Sat Dec 24 2016

This paper forms a fixed-point model which uses a predicted segmentation mask to shrink the input region and outperform the state-of-the-art by more than \(4\%\), measured by the average Dice-Sorensen Coefficient (DSC).

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U-Net Fixed-Point Quantization for Medical Image Segmentation

S. Honari, Mohammadhossein Askarihemmat, Lucas Rouhier, C. Perone, J. Cohen-Adad, Y. Savaria, J. David•Thu Aug 01 2019

This work presents a quantization method for the U-Net architecture, a popular model in medical image segmentation, and provides a flexible trade off between accuracy and memory requirement which is not provided by previous quantization methods for U- net such as TernaryNet.

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Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

A. Yuille, Yan Wang, Lingxi Xie, Yuyin Zhou, Qihang Yu, E. Fishman•Tue Sep 12 2017

We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach [46], which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.

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Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation

Kwang-Ting Cheng, X. Li, Yiqun Lin, Huimin Wu•Fri Apr 14 2023

This paper proposes a novel Compete-to-Win method (ComWin) to enhance the pseudo label quality, which can achieve the best performance on three public medical image datasets for cardiac structure segmentation, pancreas segmentation and colon tumor segmentation.

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TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions

M. Heinrich, O. Oktay, Maximilian Blendowski•Sun Jan 28 2018

This work proposes a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions and presents a key enabling technique for highly efficient DCNN inference without GPUs.

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AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

Jun Ma, Xiaoping Yang, Shangqing Liu, Yao Zhang, Song Gu, Yichi Zhang, Cheng Zhu, Qiyuan Wang, Xin Liu, Xingle An, Cheng Ge, Shucheng Cao, Qi Zhang, Yunpeng Wang, Yuhui Li, Congcong Wang, Jian He•Tue Oct 27 2020

A large and diverse abdominal CT organ segmentation dataset with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases is presented and a simple and effective method is developed for each benchmark, which can be used as out-of-the-box methods and strong baselines.

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Inter-Slice Context Residual Learning for 3D Medical Image Segmentation

Jianpeng Zhang, Yutong Xie, Yong Xia, Yan Wang•Thu Oct 29 2020

The results indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top- ranking methods in pancreas segmentation.

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Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation

U. Demir, Atahan Ozer, Y. Sahin, Gozde Unal•Thu Aug 05 2021

According to the experimental results on pancreas segmentation from Computed Tomography images, improvement in the quantitative measures is demonstrated and the proposed method shows qualitative enhancements in the segmentation maps, as demonstrated in the visual results.

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