activity
20182024
most citedMedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

35 citations · 93 across the 7 of their papers we have counts for

collaborators

7 papers

physics.med-ph2024

Anatomy-based quality metric of diffusion-weighted MRI data for accurate derivation of muscle fiber orientation

Nadya Shusharina, Xiaofeng Liu, Evangelia Kaza +3

Diffusion-weighted MRI (DW-MRI) is used to quantitatively characterize the microscopic structure of soft tissue due to the anisotropic diffusion of water in muscle. Applications su…

cs.CV2024

Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI

Xiaofeng Liu, Nadya Shusharina, Helen A Shih +3

In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The perso…

cs.CV2023★ 35 cited

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Jianning Li, Zongwei Zhou, Jiancheng Yang +154

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…

physics.med-ph2022★ 5 cited

Feasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging

Nadya Shusharina, Xiaofeng Liu, Jaume Coll-Font +5

Objective: Soft-tissue sarcoma spreads preferentially along muscle fibers. We explore the utility of deriving muscle fiber orientations from diffusion tensor MRI (DT-MRI) for defin…

cs.CV2022

ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training

Xiaofeng Liu, Fangxu Xing, Nadya Shusharina +4

Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled targe…

physics.med-ph2021★ 25 cited

Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas

Thibault Marin, Yue Zhuo, Rita Maria Lahoud +11

Background and purpose: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performe…

physics.med-ph2018★ 28 cited

The clinical target distribution: a probabilistic alternative to the clinical target volume

Nadya Shusharina, David Craft, Yen-Lin Chen +2

Definition of the clinical target volume (CTV) is one of the weakest links in the radiation therapy chain. In particular, inability to account for uncertainties is a severe limitat…