-
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
Deep neural networks have been widely adopted for automatic organ segmen...
12/25/2016 ∙ by Yuyin Zhou, et al. ∙
0
∙
share
read it
-
Liver Segmentation in Abdominal CT Images by Adaptive 3D Region Growing
Automatic liver segmentation plays an important role in computer-aided d...
02/21/2018 ∙ by Shima Rafiei, et al. ∙
0
∙
share
read it
-
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Tubular structure segmentation in medical images, e.g., segmenting vesse...
12/06/2019 ∙ by Yan Wang, et al. ∙
17
∙
share
read it
-
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT...
09/13/2017 ∙ by Qihang Yu, et al. ∙
0
∙
share
read it
-
Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors
This work presents comprehensive results to detect in the early stage th...
04/04/2020 ∙ by Zhuotun Zhu, et al. ∙
10
∙
share
read it
-
Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures
Trauma is the worldwide leading cause of death and disability in those y...
06/23/2019 ∙ by Yuyin Zhou, et al. ∙
9
∙
share
read it
-
Aorta Segmentation for Stent Simulation
Simulation of arterial stenting procedures prior to intervention allows ...
03/09/2011 ∙ by Jan Egger, et al. ∙
0
∙
share
read it
Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
06/22/2017 ∙ by Yuyin Zhou, et al. ∙
0
∙
share
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. Under a reasonable transformation function, our approach can be factorized into two stages, and each stage can be efficiently optimized via gradient back-propagation throughout the deep networks. We collect a new dataset with 131 pathological samples, which, to the best of our knowledge, is the largest set for pancreatic cyst segmentation. Without human assistance, our approach reports a 63.44 Dice-Sørensen coefficient (DSC), which is higher than the number (60.46 without deep supervision.
READ FULL TEXT
Comments
There are no comments yet.