27 citations · 66 across the 6 of their papers we have counts for
6 papers
Improved distinct bone segmentation in upper-body CT through multi-resolution networks
Eva Schnider, Julia Wolleb, Antal Huck +4
Purpose: Automated distinct bone segmentation from CT scans is widely used in planning and navigation workflows. U-Net variants are known to provide excellent results in supervised…
The secret role of undesired physical effects in accurate shape sensing with eccentric FBGs
Samaneh Manavi Roodsari, Sara Freund, Martin Angelmahr +3
Fiber optic shape sensors have enabled unique advances in various navigation tasks, from medical tool tracking to industrial applications. Eccentric fiber Bragg gratings (FBG) are…
Using Supervised Deep-Learning to Model Edge-FBG Shape Sensors
Samaneh Manavi Roodsari, Antal Huck-Horvath, Sara Freund +4
Continuum robots in robot-assisted minimally invasive surgeries provide adequate access to target anatomies that are not directly reachable through small incisions. Achieving preci…
Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images
Eva Schnider, Antal Huck, Mireille Toranelli +4
Purpose: The localisation and segmentation of individual bones is an important preprocessing step in many planning and navigation applications. It is, however, a time-consuming and…
3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies
Eva Schnider, Antal Horváth, Georg Rauter +3
Segmentation of distinct bones plays a crucial role in diagnosis, planning, navigation, and the assessment of bone metastasis. It supplies semantic knowledge to visualisation tools…
Machine learning-based method for linearization and error compensation of an absolute rotary encoder
Lorenzo Iafolla, Massimiliano Filipozzi, Sara Freund +3
The main objective of this work is to develop a miniaturized, high accuracy, single-turn absolute, rotary encoder called ASTRAS360. Its measurement principle is based on capturing…