3 citations · 7 across the 3 of their papers we have counts for
3 papers
Relational Reasoning Network (RRN) for Anatomical Landmarking
Neslisah Torosdagli, Syed Anwar, Payal Verma +4
Purpose: We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them. Towards this, we propose a new simple yet efficient deep network…
Deep Geodesic Learning for Segmentation and Anatomical Landmarking
Neslisah Torosdagli, Denise K. Liberton, Payal Verma +3
In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmark- ing. Specifically, we focus on the challenging problem of mandible segmen…
Robust and fully automated segmentation of mandible from CT scans
Neslisah Torosdagli, Denise K. Liberton, Payal Verma +3
Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. F…