17 citations · 19 across the 3 of their papers we have counts for
7 papers · 1 filter
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
On the Applicability of Registration Uncertainty
Jie Luo, Alireza Sedghi, Karteek Popuri +8
Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If…
End-to-end detection-segmentation network with ROI convolution
Zichen Zhang, Min Tang, Dana Cobzas +3
We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object l…
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
Min Tang, Zichen Zhang, Dana Cobzas +2
We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation modu…
Misdirected Registration Uncertainty
Jie Luo, Karteek Popuri, Dana Cobzas +3
Being a task of establishing spatial correspondences, medical image registration is often formalized as finding the optimal transformation that best aligns two images. Since the tr…
A deep level set method for image segmentation
Min Tang, Sepehr Valipour, Zichen Vincent Zhang +2
This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can inc…