3 papers
eess.IV2020
Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI
Syed Muhammad Anwar, Ismail Irmakci, Drew A. Torigian +5
Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and rese…
cs.CV2018
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation
Ismail Irmakci, Sarfaraz Hussein, Aydogan Savran +7
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft tissue contrast and lack of ionizing radiatio…
cs.CV2018
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…