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

By Yuyin Zhou, Lingxi Xie, Wei Shen, Yan Wang, E. Fishman, Alan L. Yuille. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016

314 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/569977bdb3f31d4b7c78ab3834fd34b370330e4e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Alan L. Yuille
2. Qi Tian
3. Luc Van Gool
4. Yi Yang
5. Dacheng Tao
6. Lingxi Xie
7. P. Heng
8. Chen Change Loy

In Radiology, Nuclear Medicine and Imaging

1. P. Heng
2. Hao Chen
3. Lequan Yu
4. Qi-Yan Dou
5. Daniel Rueckert
6. Bernhard Kainz
7. Daguang Xu
8. Ben Glocker

Citations in the corpus

• Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions (2019, methodology): “FCN has been frequently utilized for segmentation of medical and biomedical images [27, 28].”
• AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem? (2020, methodology): “In [44], CNNs were employed to segment the pancreas.”
• An application of cascaded 3D fully convolutional networks for medical image segmentation (2018, methodology): “Most methods are based on variants of FCNs (Long et al., 2015) that either employ 2D convolutional layers in a slice-by-slice fashion Roth et al. (2016b); Zhou et al. (2016b); Christ et al. (2016); Zhou et al. (2016a), 2D convolutions on orthogonal (2.5D) cross-sections (Roth et al., 2015; Prasoon e”
• Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018, unknown): “Recently, cascaded multi-stage CNN models (Roth et al., 2017, 2018; Zhou et al., 2017) have been proposed to address the problem.”
• Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges (2019, methodology): “Automatic modification of sample reweighting has been developed by using Dice loss layer and Dice coefficient [44, 62, 95].”
• Attention U-Net: Learning Where to Look for the Pancreas (2018, background): “The results in Table 3 and 4 show improved performance compared to concatenated multi-model CNN approaches [4, 24, 34] due to additional training data and richer semantic information (e.”
• H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes (2018, background): “learning strategy to reduce the overall computation time, which has also been adopted in many recognition tasks [43]–[46].”
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation (2021, methodology): “magnetic resonance (MR) [17], organ segmentation from computed tomography (CT) [7, 9, 18,20] and polyp segmentation [21] from colonoscopy videos.”
• Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks (2019, unknown): “80 [55], [56].”
• Creating Something From Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing (2020, background): “Also, the idea that ‘training a stronger model at the second time’ is related to the coarse-to-fine learning approaches [14, 49] which often adopted iteration for larger improvements.”
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation (2020, background): “Moreover, [51,69] illustrates the effectiveness of multi-view training on 2D slices, even by simply averaging multi-planar results, indicating complementary latent information exists in the biases of 2D networks.”