no code implementations • 22 Jan 2025 • Lianrui Zuo, Xin Yu, Dingjie Su, Kaiwen Xu, Aravind R. Krishnan, Yihao Liu, Shunxing Bao, Fabien Maldonado, Luigi Ferrucci, Bennett A. Landman
Body composition analysis provides valuable insights into aging, disease progression, and overall health conditions.
no code implementations • 22 Jan 2025 • Lianrui Zuo, Kaiwen Xu, Dingjie Su, Xin Yu, Aravind R. Krishnan, Yihao Liu, Shunxing Bao, Thomas Li, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman
Once trained, our approach extends the FOV of CT images in the z-direction by generating new axial slices in a zero-shot manner.
no code implementations • 10 Jan 2025 • Yanfan Zhu, Issac Lyngaas, Murali Gopalakrishnan Meena, Mary Ellen I. Koran, Bradley Malin, Daniel Moyer, Shunxing Bao, Anuj Kapadia, Xiao Wang, Bennett Landman, Yuankai Huo
A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources.
no code implementations • 29 Oct 2024 • Chenyu Gao, Michael E. Kim, Karthik Ramadass, Praitayini Kanakaraj, Aravind R. Krishnan, Adam M. Saunders, Nancy R. Newlin, Ho Hin Lee, Qi Yang, Warren D. Taylor, Brian D. Boyd, Lori L. Beason-Held, Susan M. Resnick, Lisa L. Barnes, David A. Bennett, Katherine D. Van Schaik, Derek B. Archer, Timothy J. Hohman, Angela L. Jefferson, Ivana Išgum, Daniel Moyer, Yuankai Huo, Kurt G. Schilling, Lianrui Zuo, Shunxing Bao, Nazirah Mohd Khairi, Zhiyuan Li, Christos Davatzikos, Bennett A. Landman
We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD).
no code implementations • 20 Sep 2024 • Zhiyuan Li, Tianyuan Yao, Praitayini Kanakaraj, Chenyu Gao, Shunxing Bao, Lianrui Zuo, Michael E. Kim, Nancy R. Newlin, Gaurav Rudravaram, Nazirah M. Khairi, Yuankai Huo, Kurt G. Schilling, Walter A. Kukull, Arthur W. Toga, Derek B. Archer, Timothy J. Hohman, Bennett A. Landman
We hypothesize that by this design the proposed framework can enhance the imputation performance of the dMRI scans and therefore be useful for repairing whole-brain tractography in corrupted dMRI scans with incomplete FOV.
1 code implementation • 6 Sep 2024 • Lucas W. Remedios, Han Liu, Samuel W. Remedios, Lianrui Zuo, Adam M. Saunders, Shunxing Bao, Yuankai Huo, Alvin C. Powers, John Virostko, Bennett A. Landman
We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images.
no code implementations • 15 Aug 2024 • Yanfan Zhu, Yash Singh, Khaled Younis, Shunxing Bao, Yuankai Huo
Topological data analysis (TDA) uncovers crucial properties of objects in medical imaging.
no code implementations • 30 Jun 2024 • Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Juming Xiong, Shunxing Bao, Hao Li, Mengmeng Yin, Yu Wang, Shilin Zhao, Yucheng Tang, Haichun Yang, Yuankai Huo
Panoramic image segmentation in computational pathology presents a remarkable challenge due to the morphologically complex and variably scaled anatomy.
no code implementations • 18 Jun 2024 • Xin Yu, Qi Yang, Han Liu, Ho Hin Lee, Yucheng Tang, Lucas W. Remedios, Michael E. Kim, Rendong Zhang, Shunxing Bao, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman
2D single-slice abdominal computed tomography (CT) enables the assessment of body habitus and organ health with low radiation exposure.
no code implementations • 6 May 2024 • Chenyu Gao, Shunxing Bao, Michael Kim, Nancy Newlin, Praitayini Kanakaraj, Tianyuan Yao, Gaurav Rudravaram, Yuankai Huo, Daniel Moyer, Kurt Schilling, Walter Kukull, Arthur Toga, Derek Archer, Timothy Hohman, Bennett Landman, Zhiyuan Li
We hypothesize that the imputed image with complete FOV can improve the whole-brain tractography for corrupted data with incomplete FOV.
no code implementations • 11 Jan 2024 • Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon Y. Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman
In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E.
no code implementations • 5 Jan 2024 • Ho Hin Lee, Adam M. Saunders, Michael E. Kim, Samuel W. Remedios, Lucas W. Remedios, Yucheng Tang, Qi Yang, Xin Yu, Shunxing Bao, Chloe Cho, Louise A. Mawn, Tonia S. Rex, Kevin L. Schey, Blake E. Dewey, Jeffrey M. Spraggins, Jerry L. Prince, Yuankai Huo, Bennett A. Landman
These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.
1 code implementation • 30 Sep 2023 • Ho Hin Lee, Quan Liu, Qi Yang, Xin Yu, Shunxing Bao, Yuankai Huo, Bennett A. Landman
We hypothesize that deformable convolution can be an exploratory alternative to combine all advantages from the previous operators, providing long-range dependency, adaptive spatial aggregation and computational efficiency as a foundation backbone.
no code implementations • 22 Sep 2023 • Aravind R. Krishnan, Kaiwen Xu, Thomas Li, Chenyu Gao, Lucas W. Remedios, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Kim L. Sandler, Fabien Maldonado, Ivana Isgum, Bennett A. Landman
In this study, we adopt an unpaired image translation approach to investigate harmonization between and across reconstruction kernels from different manufacturers by constructing a multipath cycle generative adversarial network (GAN).
1 code implementation • 17 Sep 2023 • Xin Yu, Qi Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, Leon Y. Cai, Ho Hin Lee, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman
We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area.
1 code implementation • 8 Sep 2023 • Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Shunxing Bao, Yuankai Huo, Bennett A. Landman
Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available.
no code implementations • 5 Sep 2023 • Muhao Liu, Chenyang Qi, Shunxing Bao, Quan Liu, Ruining Deng, Yu Wang, Shilin Zhao, Haichun Yang, Yuankai Huo
However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation.
no code implementations • 20 Aug 2023 • Shunxing Bao, Sichen Zhu, Vasantha L Kolachala, Lucas W. Remedios, Yeonjoo Hwang, Yutong Sun, Ruining Deng, Can Cui, Yike Li, Jia Li, Joseph T. Roland, Qi Liu, Ken S. Lau, Subra Kugathasan, Peng Qiu, Keith T. Wilson, Lori A. Coburn, Bennett A. Landman, Yuankai Huo
This analysis is based on data collected at the two research institutes.
no code implementations • 10 Aug 2023 • Xueyuan Li, Ruining Deng, Yucheng Tang, Shunxing Bao, Haichun Yang, Yuankai Huo
In this paper, we explore the potential of bypassing pixel-level delineation by employing the recent segment anything model (SAM) on weak box annotation in a zero-shot learning approach.
no code implementations • 10 Aug 2023 • Franklin Hu, Ruining Deng, Shunxing Bao, Haichun Yang, Yuankai Huo
While deep learning-based methods offer a solution for automatic segmentation, most suffer from a limitation: they are designed for and restricted to training on single-site, single-scale data.
no code implementations • 10 Aug 2023 • Haoju Leng, Ruining Deng, Shunxing Bao, Dazheng Fang, Bryan A. Millis, Yucheng Tang, Haichun Yang, Xiao Wang, Yifan Peng, Lipeng Wan, Yuankai Huo
The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario").
no code implementations • 3 Jul 2023 • Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang, Ruining Deng, Lucas W. Remedios, Zuhayr Asad, Joseph T. Roland, Ken S. Lau, Qi Liu, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo
Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training.
no code implementations • 1 Jul 2023 • Can Cui, Ruining Deng, Quan Liu, Tianyuan Yao, Shunxing Bao, Lucas W. Remedios, Yucheng Tang, Yuankai Huo
The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach.
no code implementations • 2 Jun 2023 • Yinchi Zhou, Ho Hin Lee, Yucheng Tang, Xin Yu, Qi Yang, Shunxing Bao, Jeffrey M. Spraggins, Yuankai Huo, Bennett A. Landman
Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed high-resolution atlas template.
no code implementations • 24 Apr 2023 • Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios, Karthik Ramadass, Aravind Krishnan, Ruining Deng, Can Cui, Shunxing Bao, Lori A. Coburn, Yuankai Huo, Bennett A. Landman
The M1 Ultra SoC was able to train the model directly on gigapixel images (16000$\times$64000 pixels, 1. 024 billion pixels) with a batch size of 1 using over 100 GB of unified memory for the process at an average speed of 1 minute and 21 seconds per batch with Tensorflow 2/Keras.
no code implementations • 9 Apr 2023 • Ruining Deng, Can Cui, Quan Liu, Tianyuan Yao, Lucas W. Remedios, Shunxing Bao, Bennett A. Landman, Lee E. Wheless, Lori A. Coburn, Keith T. Wilson, Yaohong Wang, Shilin Zhao, Agnes B. Fogo, Haichun Yang, Yucheng Tang, Yuankai Huo
However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image.
no code implementations • 1 Apr 2023 • 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
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology.
2 code implementations • 10 Mar 2023 • Ho Hin Lee, Quan Liu, Shunxing Bao, Qi Yang, Xin Yu, Leon Y. Cai, Thomas Li, Yuankai Huo, Xenofon Koutsoukos, Bennett A. Landman
We hypothesize that convolution with LK sizes is limited to maintain an optimal convergence for locality learning.
1 code implementation • 30 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
1 code implementation • 16 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.
2 code implementations • 29 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.
1 code implementation • 28 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.
1 code implementation • 28 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.
no code implementations • 28 Sep 2022 • Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Riqiang Gao, Shunxing Bao, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman
Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships.
no code implementations • 30 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.
no code implementations • 15 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.
no code implementations • 12 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.
no code implementations • 4 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.
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.
no code implementations • 19 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.
no code implementations • 3 Jun 2021 • Ho Hin Lee, Yucheng Tang, Qi Yang, Xin Yu, Shunxing Bao, Leon Y. Cai, Lucas W. Remedios, Bennett A. Landman, Yuankai Huo
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask.
1 code implementation • 23 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.
no code implementations • 23 Dec 2020 • Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias Heinrich, Jeffrey M. Spraggins, Yuankai Huo, Bennett A. Landman
However, there is no abdominal and retroperitoneal organs atlas framework for multi-contrast CT.
no code implementations • 5 Dec 2020 • Kaiwen Xu, Riqiang Gao, Mirza S. Khan, Shunxing Bao, Yucheng Tang, Steve A. Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Mattias P. Heinrich, Bennett A. Landman
For the entire study cohort, the optimized pipeline achieves a registration success rate of 91. 7%.
1 code implementation • 2 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.
no code implementations • 1 Dec 2019 • Yuankai Huo, Yucheng Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Shunxing Bao, Smita De, James G. Terry, Jeffrey J. Carr, Richard G. Abramson, Bennett A. Landman
We evaluate the effectiveness of both with and without using soft tissue window normalization on multisite CT cohorts.
no code implementations • 11 Sep 2019 • Riqiang Gao, Yuankai Huo, Shunxing Bao, Yucheng Tang, Sanja L. Antic, Emily S. Epstein, Aneri B. Balar, Steve Deppen, Alexis B. Paulson, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman
To model both regular and irregular longitudinal samples, we generalize the LSTM model with the Distanced LSTM (DLSTM) for temporally varied acquisitions.
no code implementations • 3 Apr 2019 • Anirban Bhattacharjee, Yogesh Barve, Shweta Khare, Shunxing Bao, Aniruddha Gokhale, Thomas Damiano
With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task.
2 code implementations • 28 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.
no code implementations • 21 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.
no code implementations • 10 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.
no code implementations • 10 Nov 2018 • Yuankai Huo, James G. Terry, Jiachen Wang, Vishwesh Nath, Camilo Bermudez, Shunxing Bao, Prasanna Parvathaneni, J. Jeffery Carr, Bennett A. Landman
From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0. 9272) and AUC (0. 9627).
no code implementations • 9 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.
1 code implementation • 15 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.
2 code implementations • 1 Jun 2018 • Yuankai Huo, Zhoubing Xu, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
1 code implementation • 20 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.
no code implementations • 2 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.
1 code implementation • 2 Dec 2017 • Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Andrew J. Plassard, Jiaqi Liu, Yuang Yao, Albert Assad, Richard G. Abramson, Bennett A. Landman
However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.