Zobrazeno 1 - 10
of 826
pro vyhledávání: '"Grant, P Ellen"'
Autor:
Dey, Neel, Billot, Benjamin, Wong, Hallee E., Wang, Clinton J., Ren, Mengwei, Grant, P. Ellen, Dalca, Adrian V., Golland, Polina
Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anatomical regions, and imaging protocols. We address this by creating a re
Externí odkaz:
http://arxiv.org/abs/2411.02372
The quality of fetal MRI is significantly affected by unpredictable and substantial fetal motion, leading to the introduction of artifacts even when fast acquisition sequences are employed. The development of 3D real-time fetal pose estimation approa
Externí odkaz:
http://arxiv.org/abs/2404.00132
Autor:
Abulnaga, S. Mazdak, Dey, Neel, Young, Sean I., Pan, Eileen, Hobgood, Katherine I., Wang, Clinton J., Grant, P. Ellen, Turk, Esra Abaci, Golland, Polina
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires accurate temporal placental segmentation and is confounded by fetal and
Externí odkaz:
http://arxiv.org/abs/2312.05148
Autor:
Chi, Zeen, Cong, Zhongxiao, Wang, Clinton J., Liu, Yingcheng, Turk, Esra Abaci, Grant, P. Ellen, Abulnaga, S. Mazdak, Golland, Polina, Dey, Neel
We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimation methods remain time-intensive. In this preliminary work, we frame s
Externí odkaz:
http://arxiv.org/abs/2311.02874
Autor:
Liu, Yingcheng, Karani, Neerav, Dey, Neel, Abulnaga, S. Mazdak, Xu, Junshen, Grant, P. Ellen, Turk, Esra Abaci, Golland, Polina
The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effective semi-supervised learning method for improving placenta segmentat
Externí odkaz:
http://arxiv.org/abs/2310.03870
Autor:
Dey, Neel, Abulnaga, S. Mazdak, Billot, Benjamin, Turk, Esra Abaci, Grant, P. Ellen, Dalca, Adrian V., Golland, Polina
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires
Externí odkaz:
http://arxiv.org/abs/2307.07044
Autor:
Jun, Yohan, Arefeen, Yamin, Cho, Jaejin, Fujita, Shohei, Wang, Xiaoqing, Grant, P. Ellen, Gagoski, Borjan, Jaimes, Camilo, Gee, Michael S., Bilgic, Berkin
Purpose: To develop and evaluate methods for 1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate an
Externí odkaz:
http://arxiv.org/abs/2307.01410
Autor:
He, Sheng, Bao, Rina, Li, Jingpeng, Stout, Jeffrey, Bjornerud, Atle, Grant, P. Ellen, Ou, Yangming
Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes without previously-required re-training or fine-tuning specific to each ne
Externí odkaz:
http://arxiv.org/abs/2304.09324
The combination of the U-Net based deep learning models and Transformer is a new trend for medical image segmentation. U-Net can extract the detailed local semantic and texture information and Transformer can learn the long-rang dependencies among pi
Externí odkaz:
http://arxiv.org/abs/2304.01401
Autor:
Jun, Yohan, Cho, Jaejin, Wang, Xiaoqing, Gee, Michael, Grant, P. Ellen, Bilgic, Berkin, Gagoski, Borjan
Purpose: To develop and evaluate a method for rapid estimation of multiparametric T1, T2, proton density (PD), and inversion efficiency (IE) maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (
Externí odkaz:
http://arxiv.org/abs/2302.14240