Search
CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning Ask the Fish

Everything you need to know about Computed Tomography (CT) & CT Scanning

Deep Learning: Deep Learning and the Pancreas Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and the Pancreas

-- OR --

  • “In this paper, we adopt 3D CNNs to segment the pancreas in CT images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D applications due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarse- to-fine framework for volumetric pancreas segmentation to tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes.”


    A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation 
Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
arXiv:1712.00201v1 [cs.CV] 1 Dec 2017 

  • “In this work, we proposed a novel 3D network called “ResDSN” integrated with a coarse-to-fine framework to simultaneously achieve high segmentation accuracy and low time cost. The backbone network “ResDSN” is carefully designed to only have long residual connections for efficient inference. To our best knowledge, we are the first to segment the challenging pancreas using 3D networks which leverage the rich spatial information to achieve the state-of- the-art.”

    
A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation 
Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
arXiv:1712.00201v1 [cs.CV] 1 Dec 2017 

  • “To address these issues, we propose a concise and effective framework based on 3D deep networks for pancreas segmentation, which can simultaneously achieve high seg- mentation accuracy and low time cost. Our framework is formulated in a coarse-to-fine manner. In the training stage, we first train a 3D FCN from the sub-volumes sampled from an entire CT volume. We call this ResDSN Coarse model, which aims to obtain the rough location of the target pancreas from the whole CT volume by making full use of the overall 3D context. Then, we train another 3D FCN from the sub-volumes sampled only from the ground truth bound- ing boxes of the target pancreas. We call this the ResDSN Fine model, which can refine the segmentation based on the coarse result.”


    A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation 
Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
arXiv:1712.00201v1 [cs.CV] 1 Dec 2017 

  • “This work is motivated by the difficulty of small organ segmentation. As the target is often small, it is required to 
focus on a local input region, but sometimes the network is confused due to the lack of contextual information. We present the Recurrent Saliency Transformation Network, which enjoys three advantages. (i) Benefited by a (recurrent) global energy function, it is easier to generalize our models from training data to testing data. (ii) With joint optimization over two networks, both of them get improved individually. (iii) By incorporating multi-stage visual cues, more accurate segmentation results are obtained. As the fine stage is less likely to be confused by the lack of contexts, we also observe better convergence during iterations.”


    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
arXiv:1709.04518v3 [cs.CV] 18 Nov 2017
  • “This paper presents a Recurrent Saliency Transforma- tion Network. The key innovation is a saliency transfor- mation 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.”


    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
arXiv:1709.04518v3 [cs.CV] 18 Nov 2017
  • “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. This task is important and very useful in clinical practice yet challenging due to the low contrast in boundary, the variability in location, shape and the different stages of the pancreatic cancer. Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation.”


    Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans 
Yuyin Zhou, Lingxi Xie, Elliot K. Fishman, and Alan L. Yuille 
(in) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
page 222-231
  • “This paper presents the first system for pancreatic cyst segmentation which can work without human assistance on the testing stage. Motivated by the high relevance of a cystic pancreas and a pancreatic cyst, we formulate pancreas segmentation as an explicit variable in the formulation, and introduce deep supervision to assist the network training process. The joint optimization can be factorized into two stages, making our approach very easy to implement. We collect a dataset with 131 pathological cases. Based on a coarse-to-fine segmentation algorithm, our approach produces reasonable cyst segmentation results. It is worth emphasizing that our approach does not require any extra human annotations on the testing stage, which is especially practical in assisting common patients in cheap and periodic clinical applications.”

    
Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans 
Yuyin Zhou, Lingxi Xie, Elliot K. Fishman, and Alan L. Yuille 
(in) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
page 222-231
  • “The pancreas is a highly deformable organ that has a shape and location that is greatly influenced by the presence of adjacent struc- tures. This makes automated image analysis of the pancreas extremely challenging. A number of different approaches have been taken to automated pancreas analysis, in- cluding the use of anatomic atlases, the loca- tion of the splenic and portal veins, and state- of-the-art computer science methods such as deep learning.”

    Progress in Fully Automated Abdominal CT Interpretation
Summers RM
AJR 2016; 207:67–79
  • “A recent advance in computer science is the refinement of neural networks, a type of machine learning classifier used to make decisions from data. This refine- ment, known generically as deep learn- ing but more specifically as convolutional neural networks, has shown dramatic improvements in automated intelligence applications. Initially drawing attention for impressive improvements in speech recognition and natural image interpretation, deep learning is now being applied to medical images, as described already in the sections on the pancreas and colitis.” 


    Progress in Fully Automated Abdominal CT Interpretation
Summers RM
AJR 2016; 207:67–79
© 1999-2018 Elliot K. Fishman, MD, FACR. All rights reserved.