Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets.
By Kenny H. Cha, Lubomir M. Hadjiiski, Ravi K. Samala, Heang-Ping Chan, E. Caoili, R. Cohan. 2016
247 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Surgery, Urology
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
- Daguang Xu
- Chunhua Shen
- Alan L. Yuille
- Luc Van Gool
- Ming-Hsuan Yang
- Nassir Navab
- Andrew Zisserman
- Yi Yang
In Pulmonary and Respiratory Medicine
In Surgery
In Urology
Citations in the corpus
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation (2016, background): “Post-processing approaches such as connected components analysis normally yield no improvement and therefore, more recent works, propose to use the network predictions in combination with Markov random fields [9], voting strategies [12] or more traditional approaches such as level-sets [2].”
- Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, methodology): “Eight studies [12,59,77,87,96,98,103,105] used CT Slices, three of which used data from PROMISE [75], and LIDC [166].”
- Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance (2017, methodology): “Specifically for medical image segmentation, CNNs have been applied to segment brain tumor on MR images [23], urinary bladder on CT urolography [24] left ventricle on cardiac MRI [25], and skin lesion from nondermoscopic images [26] among others.”
- Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[37] DL-CNN CT urography Urinary bladder segmentation; this method can overcome strong boundaries between two regions that have large differences of gray levels.”
- Overview of deep learning in medical imaging (2017, methodology): “In addition, a CNN was used for segmentation of the bladder in CT urography [107].”
- Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation (2018, methodology): “[39] applied a patch based CNN method to label each center pixel in a patch to segment bladder from CT scans.”
- Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks (2018, methodology): “So lot of groups in medical imaging deployed DCNNs and they were able to achieve good accuracy.(10-15) As per our knowledge for leukemia detection only, Vogado et al(16) investigated a pretrained CNN for the features extraction and used ensemble classifiers for classification of normal and blast cel”