73 citations · 305 across the 55 of their papers we have counts for
28 papers · 1 filter
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
Yingcheng Liu, Neerav Karani, Neel Dey +5
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 effe…
Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation
Dominik Hollidt, Clinton Wang, Polina Golland +1
We present a novel approach to perform 3D semantic segmentation solely from 2D supervision by leveraging Neural Radiance Fields (NeRFs). By extracting features along a surface poin…
Interpolating between Images with Diffusion Models
Clinton J. Wang, Polina Golland
One little-explored frontier of image generation and editing is the task of interpolating between two input images, a feature missing from all currently deployed image generation p…
Boundary-weighted logit consistency improves calibration of segmentation networks
Neerav Karani, Neel Dey, Polina Golland
Neural network prediction probabilities and accuracy are often only weakly-correlated. Inherent label ambiguity in training data for image segmentation aggravates such miscalibrati…
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Neel Dey, S. Mazdak Abulnaga, Benjamin Billot +4
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 structure…
Using Multiple Instance Learning to Build Multimodal Representations
Peiqi Wang, William M. Wells, Seth Berkowitz +2
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-moda…
Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging
Vivek Gopalakrishnan, Polina Golland
The use of digitally reconstructed radiographs (DRRs) to solve inverse problems such as slice-to-volume registration and 3D reconstruction is well-studied in preoperative settings.…
RadTex: Learning Efficient Radiograph Representations from Text Reports
Keegan Quigley, Miriam Cha, Ruizhi Liao +4
Automated analysis of chest radiography using deep learning has tremendous potential to enhance the clinical diagnosis of diseases in patients. However, deep learning models typica…
Volumetric Parameterization of the Placenta to a Flattened Template
S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev +3
We present a volumetric mesh-based algorithm for parameterizing the placenta to a flattened template to enable effective visualization of local anatomy and function. MRI shows pote…
Image Classification with Consistent Supporting Evidence
Peiqi Wang, Ruizhi Liao, Daniel Moyer +3
Adoption of machine learning models in healthcare requires end users' trust in the system. Models that provide additional supportive evidence for their predictions promise to facil…
Equivariant Filters for Efficient Tracking in 3D Imaging
Daniel Moyer, Esra Abaci Turk, P Ellen Grant +2
We demonstrate an object tracking method for 3D images with fixed computational cost and state-of-the-art performance. Previous methods predicted transformation parameters from con…
Bayesian Image Reconstruction using Deep Generative Models
Razvan V Marinescu, Daniel Moyer, Polina Golland
Machine learning models are commonly trained end-to-end and in a supervised setting, using paired (input, output) data. Examples include recent super-resolution methods that train…
Predictive Modeling of Anatomy with Genetic and Clinical Data
Adrian V. Dalca, Ramesh Sridharan, Mert R. Sabuncu +1
We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facili…
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
Geeticka Chauhan, Ruizhi Liao, William Wells +6
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiog…
Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy
Molin Zhang, Junshen Xu, Esra Abaci Turk +3
Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigati…
Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis
Nalini M. Singh, Juan Eugenio Iglesias, Elfar Adalsteinsson +2
We propose neural network layers that explicitly combine frequency and image feature representations and show that they can be used as a versatile building block for reconstruction…
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Adrian V. Dalca, Evan Yu, Polina Golland +3
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these…
Joint inference on structural and diffusion MRI for sequence-adaptive Bayesian segmentation of thalamic nuclei with probabilistic atlases
Juan Eugenio Iglesias, Koen Van Leemput, Polina Golland +1
Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalam…
Temporal Registration in Application to In-utero MRI Time Series
Ruizhi Liao, Esra A. Turk, Miaomiao Zhang +4
We present a robust method to correct for motion in volumetric in-utero MRI time series. Time-course analysis for in-utero volumetric MRI time series often suffers from substantial…
Placental Flattening via Volumetric Parameterization
S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev +3
We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placent…
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
Ruizhi Liao, Jonathan Rubin, Grace Lam +5
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients. Accurate assessment of…
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
Danielle F. Pace, Adrian V. Dalca, Tom Brosch +5
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a…
Medical Image Imputation from Image Collections
Adrian V. Dalca, Katherine L. Bouman, William T. Freeman +3
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large…
Keypoint Transfer for Fast Whole-Body Segmentation
Christian Wachinger, Matthew Toews, Georg Langs +2
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images. Keypoints represent automatically identified disti…