452 citations · 1.4k across the 41 of their papers we have counts for
10 papers · 1 filter
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
Qihang Yu, Dong Yang, Holger Roth +4
3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose o…
End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Jinzheng Cai, Yingda Xia, Dong Yang +3
Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentat…
NeurReg: Neural Registration and Its Application to Image Segmentation
Wentao Zhu, Andriy Myronenko, Ziyue Xu +5
Registration is a fundamental task in medical image analysis which can be applied to several tasks including image segmentation, intra-operative tracking, multi-modal image alignme…
Cardiac Segmentation of LGE MRI with Noisy Labels
Holger Roth, Wentao Zhu, Dong Yang +2
In this work, we attempt the segmentation of cardiac structures in late gadolinium-enhanced (LGE) magnetic resonance images (MRI) using only minimal supervision in a two-step appro…
Weakly supervised segmentation from extreme points
Holger Roth, Ling Zhang, Dong Yang +4
Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically r…
Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams
Chenglong Wang, Holger R. Roth, Takayuki Kitasaka +7
This paper presents a new approach for precisely estimating the renal vascular dominant region using a Voronoi diagram. To provide computer-assisted diagnostics for the pre-surgica…