Advances in auto-segmentation
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …
identify each patient's targets and anatomical structures. The efficacy and safety of the …
Multi-atlas segmentation of biomedical images: a survey
JE Iglesias, MR Sabuncu - Medical image analysis, 2015 - Elsevier
Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
[HTML][HTML] A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy
Background and purpose Delineating organs at risk (OARs) on computed tomography (CT)
images is an essential step in radiation therapy; however, it is notoriously time-consuming …
images is an essential step in radiation therapy; however, it is notoriously time-consuming …
Vision 20/20: perspectives on automated image segmentation for radiotherapy
Due to rapid advances in radiation therapy (RT), especially image guidance and treatment
adaptation, a fast and accurate segmentation of medical images is a very important part of …
adaptation, a fast and accurate segmentation of medical images is a very important part of …
A generative model for image segmentation based on label fusion
We propose a nonparametric, probabilistic model for the automatic segmentation of medical
images, given a training set of images and corresponding label maps. The resulting …
images, given a training set of images and corresponding label maps. The resulting …
[HTML][HTML] DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis
Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-
domain medical image synthesis tasks particularly due to its ability to deal with unpaired …
domain medical image synthesis tasks particularly due to its ability to deal with unpaired …
FocusNetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images
Y Gao, R Huang, Y Yang, J Zhang, K Shao, C Tao… - Medical Image …, 2021 - Elsevier
Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The
delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to …
delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to …
Interleaved 3D‐CNN s for joint segmentation of small‐volume structures in head and neck CT images
Purpose Accurate 3D image segmentation is a crucial step in radiation therapy planning of
head and neck tumors. These segmentation results are currently obtained by manual …
head and neck tumors. These segmentation results are currently obtained by manual …
Non-local statistical label fusion for multi-atlas segmentation
AJ Asman, BA Landman - Medical image analysis, 2013 - Elsevier
Multi-atlas segmentation provides a general purpose, fully-automated approach for
transferring spatial information from an existing dataset (“atlases”) to a previously unseen …
transferring spatial information from an existing dataset (“atlases”) to a previously unseen …
Clinician programmer system and method for calculating volumes of activation
DA Blum, K Carlton, A Greszler, S Kokones… - US Patent …, 2014 - Google Patents
2009-07-16 Assigned to INTELECT MEDICAL, INC. reassignment INTELECT MEDICAL,
INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS) …
INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS) …