Search Results for author: Yuankai Huo

Found 79 papers, 35 papers with code

Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training

1 code implementation30 Nov 2022 Qi Yang, Xin Yu, Ho Hin Lee, Leon Y. Cai, Kaiwen Xu, Shunxing Bao, Yuankai Huo, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, Bennett A. Landman

The proposed pipeline is effective and robust in extracting muscle groups on 2D single slice CT thigh images. The container is available for public use at https://github. com/MASILab/DA_CT_muscle_seg

Anatomy Computed Tomography (CT) +1

CircleSnake: Instance Segmentation with Circle Representation

1 code implementation2 Nov 2022 Ethan H. Nguyen, Haichun Yang, Zuhayr Asad, Ruining Deng, Agnes B. Fogo, Yuankai Huo

Circle representation has recently been introduced as a medical imaging optimized representation for more effective instance object detection on ball-shaped medical objects.

Instance Segmentation object-detection +2

Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation

1 code implementation16 Oct 2022 Ho Hin Lee, Yucheng Tang, Han Liu, Yubo Fan, Leon Y. Cai, Qi Yang, Xin Yu, Shunxing Bao, Yuankai Huo, Bennett A. Landman

We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets.

Computed Tomography (CT) Contrastive Learning

3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation

1 code implementation29 Sep 2022 Ho Hin Lee, Shunxing Bao, Yuankai Huo, Bennett A. Landman

Hierarchical transformers (e. g., Swin Transformers) reintroduced several ConvNet priors and further enhanced the practical viability of adapting volumetric segmentation in 3D medical datasets.

Image Segmentation Medical Image Segmentation +2

Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models

1 code implementation28 Sep 2022 Xin Yu, Qi Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, LeonY. Cai, Ho Hin Lee, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman

External experiments on 20 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset that contains longitudinal single abdominal slices validate that our method can harmonize the slice positional variance in terms of muscle and visceral fat area.

Computed Tomography (CT)

UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

1 code implementation28 Sep 2022 Xin Yu, Qi Yang, Yinchi Zhou, Leon Y. Cai, Riqiang Gao, Ho Hin Lee, Thomas Li, Shunxing Bao, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Zizhao Zhang, Yuankai Huo, Bennett A. Landman, Yucheng Tang

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.

Brain Segmentation Image Segmentation +2

Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning

1 code implementation30 Aug 2022 Tianyuan Yao, Chang Qu, Jun Long, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Zuhayr Asad, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Haichun Yang, Catie Chang, Yuankai Huo

In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation.

Contrastive Learning Image Augmentation +2

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

1 code implementation15 Aug 2022 Ruining Deng, Can Cui, Lucas W. Remedios, Shunxing Bao, R. Michael Womick, Sophie Chiron, Jia Li, Joseph T. Roland, Ken S. Lau, Qi Liu, Keith T. Wilson, Yaohong Wang, Lori A. Coburn, Bennett A. Landman, Yuankai Huo

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations.

whole slide images

Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation

1 code implementation27 Jun 2022 Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Jun Long, Zuhayr Asad, R. Michael Womick, Zheyu Zhu, Agnes B. Fogo, Shilin Zhao, Haichun Yang, Yuankai Huo

The contribution of this paper is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining.

Image Segmentation Semantic Segmentation

Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining

1 code implementation31 May 2022 Tianyuan Yao, Yuzhe Lu, Jun Long, Aadarsh Jha, Zheyu Zhu, Zuhayr Asad, Haichun Yang, Agnes B. Fogo, Yuankai Huo

To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining.

Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation

no code implementations12 May 2022 Ho Hin Lee, Yucheng Tang, Riqiang Gao, Qi Yang, Xin Yu, Shunxing Bao, James G. Terry, J. Jeffrey Carr, Yuankai Huo, Bennett A. Landman

In this paper, we propose a novel unsupervised approach that leverages pairwise contrast-enhanced CT (CECT) context to compute non-contrast segmentation without ground-truth label.

Pseudo Label

Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review

no code implementations25 Mar 2022 Can Cui, Haichun Yang, Yaohong Wang, Shilin Zhao, Zuhayr Asad, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice.

Decision Making

Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data

no code implementations8 Mar 2022 Can Cui, Han Liu, Quan Liu, Ruining Deng, Zuhayr Asad, Yaohong WangShilin Zhao, Haichun Yang, Bennett A. Landman, Yuankai Huo

Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e. g., one or more modalities might not be collected for a patient).

Survival Prediction

Characterizing Renal Structures with 3D Block Aggregate Transformers

no code implementations4 Mar 2022 Xin Yu, Yucheng Tang, Yinchi Zhou, Riqiang Gao, Qi Yang, Ho Hin Lee, Thomas Li, Shunxing Bao, Yuankai Huo, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Bennett A. Landman

Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.

Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification

1 code implementation31 Jan 2022 Yuzhe Lu, Haichun Yang, Zuhayr Asad, Zheyu Zhu, Tianyuan Yao, Jiachen Xu, Agnes B. Fogo, Yuankai Huo

Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease.


Semi-Supervised Contrastive Learning for Remote Sensing: Identifying Ancient Urbanization in the South Central Andes

no code implementations13 Dec 2021 Jiachen Xu, James Zimmer-Dauphinee, Quan Liu, Yuxuan Shi, Steven Wernke, Yuankai Huo

The development of self-supervised learning (e. g., contrastive learning) offers a scalable learning scheme in locating archaeological sites using unlabeled satellite and historical aerial images.

Contrastive Learning Self-Supervised Learning

Circle Representation for Medical Object Detection

1 code implementation22 Oct 2021 Ethan H. Nguyen, Haichun Yang, Ruining Deng, Yuzhe Lu, Zheyu Zhu, Joseph T. Roland, Le Lu, Bennett A. Landman, Agnes B. Fogo, Yuankai Huo

Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant.

Medical Object Detection object-detection

Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging

no code implementations MICCAI Workshop COMPAY 2021 Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Sophie Chiron, Ilwoo Lyu, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo

Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed 'N-to-N' strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e. g., '(N-1)-to-1', '(N-2)-to-2'); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF.

Image Generation Specificity

Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation

no code implementations26 Aug 2021 Cam Nguyen, Zuhayr Asad, Yuankai Huo

For a more comprehensive analysis, we also compare the transformer-based models with six major traditional CNN-based models.

Image Segmentation Tumor Segmentation +1

Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective

no code implementations25 Jul 2021 Riqiang Gao, Yucheng Tang, Kaiwen Xu, Ho Hin Lee, Steve Deppen, Kim Sandler, Pierre Massion, Thomas A. Lasko, Yuankai Huo, Bennett A. Landman

To our knowledge, it is the first generative adversarial model that addresses multi-modal missing imputation by modeling the joint distribution of image and non-image data.


Compound Figure Separation of Biomedical Images with Side Loss

1 code implementation19 Jul 2021 Tianyuan Yao, Chang Qu, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Catie Chang, Haichun Yang, Yuankai Huo

Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations.

Contrastive Learning Image Augmentation +1

VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning

no code implementations22 Jun 2021 Mengyang Zhao, Quan Liu, Aadarsh Jha, Ruining Deng, Tianyuan Yao, Anita Mahadevan-Jansen, Matthew J. Tyska, Bryan A. Millis, Yuankai Huo

Recently, pixel embedding-based cell instance segmentation and tracking provided a neat and generalizable computing paradigm for understanding cellular dynamics.

3D Instance Segmentation Semantic Segmentation

SimTriplet: Simple Triplet Representation Learning with a Single GPU

1 code implementation9 Mar 2021 Quan Liu, Peter C. Louis, Yuzhe Lu, Aadarsh Jha, Mengyang Zhao, Ruining Deng, Tianyuan Yao, Joseph T. Roland, Haichun Yang, Shilin Zhao, Lee E. Wheless, Yuankai Huo

The contribution of the paper is three-fold: (1) The proposed SimTriplet method takes advantage of the multi-view nature of medical images beyond self-augmentation; (2) The method maximizes both intra-sample and inter-sample similarities via triplets from positive pairs, without using negative samples; and (3) The recent mix precision training is employed to advance the training by only using a single GPU with 16GB memory.

Contrastive Learning Representation Learning +1

Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation

1 code implementation16 Jan 2021 Yuzhe Lu, Haichun Yang, Zheyu Zhu, Ruining Deng, Agnes B. Fogo, Yuankai Huo

Different from the recently proposed CutMix method, the CircleMix augmentation is optimized for the ball-shaped biomedical objects, such as glomeruli.

Classification Data Augmentation +1

WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19

1 code implementation4 Jan 2021 Zekun Wang, Pengwei Wang, Peter C. Louis, Lee E. Wheless, Yuankai Huo

The serverless edge-computing design minimizes the extra hardware costs (e. g., specific devices or cloud computing servers).


ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations

no code implementations3 Jan 2021 Quan Liu, Isabella M. Gaeta, Mengyang Zhao, Ruining Deng, Aadarsh Jha, Bryan A. Millis, Anita Mahadevan-Jansen, Matthew J. Tyska, Yuankai Huo

Contribution: The contribution of this paper is three-fold: (1) the proposed method aggregates adversarial simulations and single-stage pixel-embedding based deep learning; (2) the method is assessed with both the cellular (i. e., HeLa cells) and subcellular (i. e., microvilli) objects; and (3) to the best of our knowledge, this is the first study to explore annotation-free instance segmentation and tracking study for microscope videos.

Instance Segmentation Semantic Segmentation

RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior

1 code implementation23 Dec 2020 Ho Hin Lee, Yucheng Tang, Shunxing Bao, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

We combine the anatomical prior with corresponding extracted patches to preserve the anatomical locations and boundary information for performing high-resolution segmentation across all organs in a single model.

Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis

no code implementations22 Dec 2020 Ye Chen, Yuankai Huo

Multi-object tracking (MOT) in computer vision and cell tracking in biomedical image analysis are two similar research fields, whose common aim is to achieve instance level object detection/segmentation and associate such objects across different video frames.

Multi-Object Tracking Multiple Object Tracking +2

CaCL: Class-aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns

1 code implementation2 Nov 2020 Ruining Deng, Quan Liu, Shunxing Bao, Aadarsh Jha, Catie Chang, Bryan A. Millis, Matthew J. Tyska, Yuankai Huo

Our contribution is three-fold: (1) we approach the weakly supervised segmentation from a novel codebook learning perspective; (2) the CaCL algorithm segments diffuse image patterns rather than focal objects; and (3) the proposed algorithm is implemented in a multi-task framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) via joint image reconstruction, classification, feature embedding, and segmentation.

Image Reconstruction Weakly supervised segmentation

GAN based Unsupervised Segmentation: Should We Match the Exact Number of Objects

no code implementations22 Oct 2020 Quan Liu, Isabella M. Gaeta, Bryan A. Millis, Matthew J. Tyska, Yuankai Huo

To match the number of objects at the micro-level, the novel fluorescence-based micro-level matching approach was presented.

EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology

1 code implementation28 Jul 2020 Zheyu Zhu, Yuzhe Lu, Ruining Deng, Haichun Yang, Agnes B. Fogo, Yuankai Huo

Inspired by the recent "human-in-the-loop" strategy, we developed EasierPath, an open-source tool to integrate human physicians and deep learning algorithms for efficient large-scale pathological image quantification as a loop.

object-detection Object Detection +1

Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking

1 code implementation28 Jul 2020 Mengyang Zhao, Aadarsh Jha, Quan Liu, Bryan A. Millis, Anita Mahadevan-Jansen, Le Lu, Bennett A. Landman, Matthew J. Tyskac, Yuankai Huo

With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm.

Cell Segmentation Instance Segmentation +1

Instance Segmentation for Whole Slide Imaging: End-to-End or Detect-Then-Segment

2 code implementations7 Jul 2020 Aadarsh Jha, Haichun Yang, Ruining Deng, Meghan E. Kapp, Agnes B. Fogo, Yuankai Huo

In this paper, we assess if the end-to-end instance segmentation framework is optimal for high-resolution WSI objects by comparing Mask-RCNN with our proposed detect-then-segment framework.

Instance Segmentation Semantic Segmentation

Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images

no code implementations10 Jun 2020 Ruining Deng, Haichun Yang, Aadarsh Jha, Yuzhe Lu, Peng Chu, Agnes B. Fogo, Yuankai Huo

However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI.

Association Multi-Object Tracking +1

CircleNet: Anchor-free Detection with Circle Representation

1 code implementation3 Jun 2020 Haichun Yang, Ruining Deng, Yuzhe Lu, Zheyu Zhu, Ye Chen, Joseph T. Roland, Le Lu, Bennett A. Landman, Agnes B. Fogo, Yuankai Huo

In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus.

object-detection Object Detection

JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans

no code implementations ECCV 2020 Fengze Liu, Jingzheng Cai, Yuankai Huo, Chi-Tung Cheng, Ashwin Raju, Dakai Jin, Jing Xiao, Alan Yuille, Le Lu, Chien-Hung Liao, Adam P. Harrison

We extensively evaluate our JSSR system on a large-scale medical image dataset containing 1, 485 patient CT imaging studies of four different phases (i. e., 5, 940 3D CT scans with pathological livers) on the registration, segmentation and synthesis tasks.

Image Registration Multi-Task Learning +1

The Value of Nullspace Tuning Using Partial Label Information

no code implementations17 Mar 2020 Colin B. Hansen, Vishwesh Nath, Diego A. Mesa, Yuankai Huo, Bennett A. Landman, Thomas A. Lasko

But in some learning problems, partial label information can be inferred from otherwise unlabeled examples and used to further improve the model.

Outlier Guided Optimization of Abdominal Segmentation

no code implementations10 Feb 2020 Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e. g., exemplars for which the baseline algorithm failed) or inliers (e. g., exemplars for which the baseline algorithm worked).

Active Learning Computed Tomography (CT)

Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

1 code implementation21 Jan 2020 Jinzheng Cai, Adam P. Harrison, Youjing Zheng, Ke Yan, Yuankai Huo, Jing Xiao, Lin Yang, Le Lu

This is the goal of our work, where we develop a powerful system to harvest missing lesions from the DeepLesion dataset at high precision.

Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection

1 code implementation16 Dec 2019 Yiyuan Yang, Riqiang Gao, Yucheng Tang, Sanja L. Antic, Steve Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman

To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training.

Multi-Task Learning

Contrast Phase Classification with a Generative Adversarial Network

no code implementations14 Nov 2019 Yucheng Tang, Ho Hin Lee, Yuchen Xu, Olivia Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Camilo Bermudez, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy.

Anatomy Classification +3

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

no code implementations12 Nov 2019 Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method.

Image Segmentation Medical Image Segmentation +1

Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations

1 code implementation23 Jun 2019 Yuankai Huo, James G. Terry, Jiachen Wang, Sangeeta Nair, Thomas A. Lasko, Barry I. Freedman, J. Jeffery Carr, Bennett A. Landman

Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD).

Computed Tomography (CT) Liver Segmentation

3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles

2 code implementations28 Mar 2019 Yuankai Huo, Zhoubing Xu, Yunxi Xiong, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman

To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.

Brain Segmentation

Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics

no code implementations21 Feb 2019 Jiachen Wang, Riqiang Gao, Yuankai Huo, Shunxing Bao, Yunxi Xiong, Sanja L. Antic, Travis J. Osterman, Pierre P. Massion, Bennett A. Landman

The results show that the AUC obtained from clinical demographics alone was 0. 635 while the attention network alone reached an accuracy of 0. 687.

Computed Tomography (CT)

Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols

no code implementations7 Jan 2019 Yunxi Xiong, Yuankai Huo, Jiachen Wang, L. Taylor Davis, Maureen McHugo, Bennett A. Landman

Recently, we obtained a clinically acquired, multi-sequence MRI brain cohort with 1480 clinically acquired, de-identified brain MRI scans on 395 patients using seven different MRI protocols.

Brain Segmentation

Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network

no code implementations10 Dec 2018 Cailey I. Kerley, Yuankai Huo, Shikha Chaganti, Shunxing Bao, Mayur B. Patel, Bennett A. Landman

For instance, in a typical sample of clinical TBI imaging cohort, only ~15% of CT scans actually contain whole brain CT images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images.

Computed Tomography (CT) Medical Image Retrieval +1

Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps

no code implementations26 Nov 2018 Camilo Bermudez, William Rodriguez, Yuankai Huo, Allison E. Hainline, Rui Li, Robert Shults, Pierre D. DHaese, Peter E. Konrad, Benoit M. Dawant, Bennett A. Landman

We show an improvement in the classification of intraoperative stimulation coordinates as a positive response in reduction of symptoms with AUC of 0. 670 compared to a baseline registration-based approach, which achieves an AUC of 0. 627 (p < 0. 01).

Anatomy BIG-bench Machine Learning +1

Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks

no code implementations9 Nov 2018 Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Hyeonsoo Moon, Prasanna Parvathaneni, Tamara K. Moyo, Michael R. Savona, Albert Assad, Richard G. Abramson, Bennett A. Landman

A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network.

Splenomegaly Segmentation On Multi-Modal Mri

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

1 code implementation15 Oct 2018 Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara K. Moyo, Michael R. Savona, Richard G. Abramson, Bennett A. Landman

SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality.

Image Segmentation Semantic Segmentation

Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation

no code implementations6 Jun 2018 Yuankai Huo, Katherine Swett, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman

By indexing the dictionary, the whole brain probabilistic atlases adapt to each new subject quickly and can be used as spatial priors for visualization and processing.


Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images

no code implementations25 May 2018 Zhoubing Xu, Yuankai Huo, Jin-Hyeong Park, Bennett Landman, Andy Milkowski, Sasa Grbic, Shaohua Zhou

However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e. g., low contrast and large variations, but also the heterogeneity across tasks, i. e., one classification task for all views, and then one landmark detection task for each relevant view.

Classification General Classification +1

Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth

1 code implementation20 Dec 2017 Yuankai Huo, Zhoubing Xu, Shunxing Bao, Albert Assad, Richard G. Abramson, Bennett A. Landman

Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT.

Image-to-Image Translation Medical Image Segmentation +1

Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation

no code implementations2 Dec 2017 Yuankai Huo, Shunxing Bao, Prasanna Parvathaneni, Bennett A. Landman

Herein, the MaCRUISE surface parcellation (MaCRUISEsp) method is proposed to perform the surface parcellation upon the inner, central and outer surfaces that are reconstructed from MaCRUISE.

Brain Segmentation Surface Reconstruction

4D Multi-atlas Label Fusion using Longitudinal Images

no code implementations29 Aug 2017 Yuankai Huo, Susan M. Resnick, Bennett A. Landman

(2) The proposed algorithm is a longitudinal generalization of a lead-ing joint label fusion method (JLF) that has proven adaptable to a wide variety of applications.

Image Segmentation Medical Image Segmentation +1

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