activity
20162026
most citedVerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

386 citations · 480 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV2022

Landmark-free Statistical Shape Modeling via Neural Flow Deformations

David Lüdke, Tamaz Amiranashvili, Felix Ambellan +3

Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shap…

cs.CV2021

Geodesic B-Score for Improved Assessment of Knee Osteoarthritis

Felix Ambellan, Stefan Zachow, Christoph von Tycowicz

Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent mea…

cs.CV2021

Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation

Manish Sahu, Anirban Mukhopadhyay, Stefan Zachow

Purpose: Segmentation of surgical instruments in endoscopic videos is essential for automated surgical scene understanding and process modeling. However, relying on fully supervise…

cs.CV2020

Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation

Manish Sahu, Ronja Strömsdörfer, Anirban Mukhopadhyay +1

Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised…

cs.CV2020★ 386 cited

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…

cs.CV2016★ 30 cited

Tool and Phase recognition using contextual CNN features

Manish Sahu, Anirban Mukhopadhyay, Angelika Szengel +1

A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here. In addition, me…

cs.CV2016★ 48 cited

Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds

Florian Bernard, Luis Salamanca, Johan Thunberg +7

The reconstruction of an object's shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measur…