Adrian V Dalca
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Explore the profile of Adrian V Dalca including associated specialties, affiliations and a list of published articles.
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61
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1399
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Recent Articles
1.
Gopinath K, Hoopes A, Alexander D, Arnold S, Balbastre Y, Billot B, et al.
Imaging Neurosci (Camb)
. 2025 Jan;
2():1-22.
PMID: 39850547
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware...
2.
Singh N, Dey N, Hoffmann M, Fischl B, Adalsteinsson E, Frost R, et al.
Proc Mach Learn Res
. 2024 Oct;
227:368-381.
PMID: 39415845
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image...
3.
Kelley W, Ngo N, Dalca A, Fischl B, Zollei L, Hoffmann M
Proc IEEE Int Symp Biomed Imaging
. 2024 Oct;
2024.
PMID: 39371473
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition...
4.
Li J, Tuckute G, Fedorenko E, Edlow B, Dalca A, Fischl B
Med Image Anal
. 2024 Aug;
98:103292.
PMID: 39173411
Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal...
5.
Hoffmann M, Hoopes A, Greve D, Fischl B, Dalca A
Imaging Neurosci (Camb)
. 2024 Jul;
2:1-33.
PMID: 39015335
Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a...
6.
Gazula H, Tregidgo H, Billot B, Balbastre Y, Williams-Ramirez J, Herisse R, et al.
Elife
. 2024 Jun;
12.
PMID: 38896568
We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our...
7.
Kazi A, Mora J, Fischl B, Dalca A, Aganj I
Graphs Biomed Image Anal Overlapped Cell Tissue Dataset Histopathol (2023)
. 2024 Apr;
14373:27-36.
PMID: 38665679
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph...
8.
Kelley W, Ngo N, Dalca A, Fischl B, Zollei L, Hoffmann M
ArXiv
. 2024 Mar;
PMID: 38463507
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition...
9.
Bourached A, Bonkhoff A, Schirmer M, Regenhardt R, Bretzner M, Hong S, et al.
Brain Commun
. 2024 Jan;
6(1):fcae007.
PMID: 38274570
Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 10-10 examples. It is currently unknown whether deep learning can also enhance predictions...
10.
Hoffmann M, Billot B, Iglesias J, Fischl B, Dalca A
Proc IEEE Int Symp Biomed Imaging
. 2024 Jan;
2023:899-903.
PMID: 38213549
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to magnetic resonance imaging (MRI) contrast. While classical methods accurately estimate the spatial correspondence...