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Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation [article]

Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille
2018 arXiv   pre-print
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans.  ...  To alleviate this, researchers proposed a coarse-to-fine approach, which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage.  ...  Acknowledgements: This paper was supported by the Lustgarten Foundation for Pancreatic Cancer Research.  ... 
arXiv:1709.04518v4 fatcat:ceoss6e35rdmnkvyua652swjy4

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

Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans.  ...  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.  ...  Acknowledgements: This paper was supported by the Lustgarten Foundation for Pancreatic Cancer Research.  ... 
doi:10.1109/cvpr.2018.00864 dblp:conf/cvpr/YuXWZFY18 fatcat:32vs6e335rd7bo2xr2kufyqvxq

A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation [article]

Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
2018 arXiv   pre-print
We propose a novel 3D-based coarse-to-fine framework to effectively and efficiently tackle these challenges.  ...  In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images.  ...  The pancreas usually occupies a small region in a whole CT scan. Best viewed in color.  ... 
arXiv:1712.00201v2 fatcat:llskl4f74rdc5aigp7c3oh6zsq

Multi-scale U-like network with attention mechanism for automatic pancreas segmentation

Yingjing Yan, Defu Zhang, Jianjun Hu
2021 PLoS ONE  
In recent years, the rapid development of deep neural networks has made great progress in automatic organ segmentation from abdominal CT scans.  ...  However, automatic segmentation for small organs (e.g., the pancreas) is still a challenging task.  ...  into another 2D FCN for fine segmentation.  ... 
doi:10.1371/journal.pone.0252287 pmid:34043732 fatcat:goihhrd3ardkbdqtbd3lo3ktqq

An application of cascaded 3D fully convolutional networks for medical image segmentation

Holger R. Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori
2018 Computerized Medical Imaging and Graphics  
We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels.  ...  To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN.  ...  In this work, we utilized 3D U-Net for the segmentation of CT scans.  ... 
doi:10.1016/j.compmedimag.2018.03.001 pmid:29573583 fatcat:6ihzswvpurdfval676ptnebv4q

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

Dan Li, Chuda Xiao, Yang Liu, Zhuo Chen, Haseeb Hassan, Liyilei Su, Jun Liu, Haoyu Li, Weiguo Xie, Wen Zhong, Bingding Huang
2022 Diagnostics  
Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited.  ...  Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney  ...  Acknowledgments: We would like to thank the students from Shenzhen Technology University whom were involved in this project's annotation phase and the radiologists from the First Affiliated Hospital of  ... 
doi:10.3390/diagnostics12081788 pmid:35892498 pmcid:PMC9330428 fatcat:amkihaminfef3mu4b6rpgwx7oe

Liver segmentation in CT images using three dimensional to two dimensional fully convolutional network [article]

Shima Rafiei, Ebrahim Nasr-Esfahani, S.M.Reza Soroushmehr, Nader Karimi, Shadrokh Samavi, Kayvan Najarian
2018 arXiv   pre-print
The need for CT scan analysis is growing for pre-diagnosis and therapy of abdominal organs.  ...  In this paper we propose an efficient liver segmentation with our 3D to 2D fully connected network (3D-2D-FCN).  ...  [5] , apply sliding windowbased networks to segment pancreas organ hierarchically. They use ConvNet networks in the form of coarse-to-fine to classify pancreas patches and superpixels.  ... 
arXiv:1802.07800v2 fatcat:bsueflei3je3vii2htrrwnwmqu

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

Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert
2018 arXiv   pre-print
The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation.  ...  We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.  ...  Evaluation Datasets: For the experiments, two different CT abdominal datasets are used: (I) 150 abdominal 3D CT scans acquired from patients diagnosed with gastric cancer (CT -150).  ... 
arXiv:1804.03999v3 fatcat:pbdj3j74vrbfxmj423ikp232py

RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior [article]

Ho Hin Lee, Yucheng Tang, Shunxing Bao, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
2020 arXiv   pre-print
Performing coarse-to-fine abdominal multi-organ segmentation facilitates to extract high-resolution segmentation minimizing the lost of spatial contextual information.  ...  However, current coarse-to-refine approaches require a significant number of models to perform single organ refine segmentation corresponding to the extracted organ region of interest (ROI).  ...  Both 2D and 3D-patch based learning methods target the single organ approaches, and it is still challenging to extend to the whole abdominal interest regions.  ... 
arXiv:2012.12425v2 fatcat:zz7ur32iljfhpkl5ldjnpe255y

Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation [article]

Ho Hin Lee, Yucheng Tang, Riqiang Gao, Qi Yang, Xin Yu, Shunxing Bao, James G. Terry, J. Jeffrey Carr, Yuankai Huo, Bennett A. Landman
2022 arXiv   pre-print
We validate our approach on multi-organ segmentation with paired non-contrast & contrast-enhanced CT scans using five-fold cross-validation.  ...  In this paper, we propose a novel unsupervised approach that leverages pairwise contrast-enhanced CT (CECT) context to compute non-contrast segmentation without ground-truth label.  ...  Roth et al. proposed a coarse-to-fine method that roughly defines the local region to extract representation for refined segmentation [23] .  ... 
arXiv:2205.05898v1 fatcat:zmamp2qf4rdrrcg7fyhbbn4r3i

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images [article]

Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, Daniel Rueckert
2019 arXiv   pre-print
For segmentation, the proposed architecture is evaluated on two large 3D CT abdominal datasets with manual annotations for multiple organs.  ...  For classification, we demonstrate the use case of AGs in scan plane detection for fetal ultrasound screening.  ...  Acknowledgements We thank the volunteers, radiographers and experts for providing manually annotated datasets, Wellcome Trust IEH Award [102431], NVIDIA for their GPU donations, and Intel.  ... 
arXiv:1808.08114v2 fatcat:xf5fimvy6begxbpevpxkrlbinm

A Semiautomated Deep Learning Approach for Pancreas Segmentation

Meixiang Huang, Chongfei Huang, Jing Yuan, Dexing Kong, Jialin Peng
2021 Journal of Healthcare Engineering  
Accurate pancreas segmentation from 3D CT volumes is important for pancreas diseases therapy.  ...  Moreover, a nonlinear Dice-based loss function is designed to tackle the class-imbalanced problem in the pancreas segmentation.  ...  Since the pancreas is a small, soft organ in the abdomen, most pancreas segmentation algorithms based on convolutional neural network (CNN) provide iterative algorithms [15] in a coarse-to-fine manner  ... 
doi:10.1155/2021/3284493 pmid:34306587 pmcid:PMC8272661 fatcat:g353u5lmundhzjksd4uxypqxky

Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans [article]

Yuyin Zhou, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille
2017 arXiv   pre-print
Automatic segmentation of an organ and its cystic region is a prerequisite of computer-aided diagnosis. In this paper, we focus on pancreatic cyst segmentation in abdominal CT scan.  ...  We collect a new dataset with 131 pathological samples, which, to the best of our knowledge, is the largest set for pancreatic cyst segmentation.  ...  This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research. We thank Dr. Seyoun Park for enormous help.  ... 
arXiv:1706.07346v1 fatcat:tycjom7etzhfhanlakarg4bpzu

Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors [article]

Tahereh Mahmoudi, Zahra Mousavi Kouzehkanan, AmirReza Radmard, Rahele Kafieh, Aneseh Salehnia, Amir H. Davarpanah, Hossein Arabalibeik, Alireza Ahmadian
2021 bioRxiv   pre-print
This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well.  ...  Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation.  ...  Zhu et. al presented a multi scale coarse to fine segmentation for screening PDAC in CT images, achieving a dice score of 57.3% for PDAC mass segmentation (7).  ... 
doi:10.1101/2021.06.09.447508 fatcat:7xa56ifg55bj3ix6zauwjg34ji

Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation [article]

Yading Yuan
2017 arXiv   pre-print
A simple CDNN model is firstly trained to provide a quick but coarse segmentation of the liver on the entire CT volume, then another CDNN is applied to the liver region for fine liver segmentation.  ...  MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) provides a common platform for comparing different automatic algorithms on contrast-enhanced abdominal CT images in tasks including 1) liver segmentation  ...  In the first step, a simple CDNN model is trained to obtain a quick but coarse segmentation of the liver on the entire 3D CT volume; then another CDNN is applied to the liver region for fine liver segmentation  ... 
arXiv:1710.04540v1 fatcat:zppycxvqerguhcs3y2b3vk6ne4
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