33 citations · 36 across the 5 of their papers we have counts for
5 papers
μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation
Pierangela Bruno, Edoardo De Rose, Carlo Adornetto +12
X-ray computed microtomography (μ-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples. These im…
Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming
Pierangela Bruno, Francesco Calimeri, Cinzia Marte +1
Although the availability of a large amount of data is usually given for granted, there are relevant scenarios where this is not the case; for instance, in the biomedical/healthcar…
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Tobias Roß, Pierangela Bruno, Annika Reinke +12
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step fo…
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
Lena Maier-Hein, Martin Wagner, Tobias Ross +30
Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracki…
Robust Medical Instrument Segmentation Challenge 2019
Tobias Ross, Annika Reinke, Peter M. Full +47
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tr…