Search Results for author: Dakai Jin

Found 30 papers, 7 papers with code

Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification

no code implementations28 Jul 2023 Ke Yan, Dakai Jin, Dazhou Guo, Minfeng Xu, Na Shen, Xian-Sheng Hua, Xianghua Ye, Le Lu

Motivated by this observation, we propose a novel end-to-end framework to improve LN detection performance by leveraging their station information.

Anatomy Multi-Task Learning

SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid

1 code implementation19 Jul 2023 Zi Li, Lin Tian, Tony C. W. Mok, Xiaoyu Bai, Puyang Wang, Jia Ge, Jingren Zhou, Le Lu, Xianghua Ye, Ke Yan, Dakai Jin

Estimating displacement vector field via a cost volume computed in the feature space has shown great success in image registration, but it suffers excessive computation burdens.

Image Registration

Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images

no code implementations7 Jul 2023 Xiaoyu Bai, Fan Bai, Xiaofei Huo, Jia Ge, Tony C. W. Mok, Zi Li, Minfeng Xu, Jingren Zhou, Le Lu, Dakai Jin, Xianghua Ye, JingJing Lu, Ke Yan

We then use this SAM to identify corresponding regions on paired images using robust grid-points matching, followed by a point-set based affine/rigid registration, and a deformable fine-tuning step to produce registered paired images.

Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning

no code implementations15 Jun 2023 Puyang Wang, Dazhou Guo, Dandan Zheng, Minghui Zhang, Haogang Yu, Xin Sun, Jia Ge, Yun Gu, Le Lu, Xianghua Ye, Dakai Jin

Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthma and lung cancer.

Anatomy Computed Tomography (CT) +3

Deep Implicit Statistical Shape Models for 3D Medical Image Delineation

1 code implementation7 Apr 2021 Ashwin Raju, Shun Miao, Dakai Jin, Le Lu, Junzhou Huang, Adam P. Harrison

DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.

Descriptive Liver Segmentation +1

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy

no code implementations27 Aug 2020 Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung-Ying Ho, Xianghua Ye, Dazhou Guo, Chun-Hung Chao, Jing Xiao, Alan Yuille, Le Lu

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.

Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets

no code implementations28 May 2020 Ke Yan, Jinzheng Cai, Adam P. Harrison, Dakai Jin, Jing Xiao, Le Lu

First, we learn a multi-head multi-task lesion detector using all datasets and generate lesion proposals on DeepLesion.

Ranked #5 on Medical Object Detection on DeepLesion (using extra training data)

Lesion Detection Medical Object Detection +1

Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification

no code implementations27 May 2020 Zhuotun Zhu, Ke Yan, Dakai Jin, Jinzheng Cai, Tsung-Ying Ho, Adam P. Harrison, Dazhou Guo, Chun-Hung Chao, Xianghua Ye, Jing Xiao, Alan Yuille, Le Lu

We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.

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 +2

Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search

no code implementations CVPR 2020 Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung-Ying Ho, Adam P. Harrison, Chun-Hung Chao, Jing Xiao, Alan Yuille, Chien-Yu Lin, Le Lu

This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories.

Anatomy Neural Architecture Search +1

Weakly Supervised Universal Fracture Detection in Pelvic X-rays

no code implementations4 Sep 2019 Yirui Wang, Le Lu, Chi-Tung Cheng, Dakai Jin, Adam P. Harrison, Jing Xiao, Chien-Hung Liao, Shun Miao

In this paper, we propose a two-stage hip and pelvic fracture detection method that executes localized fracture classification using weakly supervised ROI mining.

Multiple Instance Learning

Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk

no code implementations4 Sep 2019 Dakai Jin, Dazhou Guo, Tsung-Ying Ho, Adam P. Harrison, Jing Xiao, Chen-Kan Tseng, Le Lu

Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT).

Data Augmentation

White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections

no code implementations19 Mar 2018 Dakai Jin, Ziyue Xu, Adam P. Harrison, Daniel J. Mollura

Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders.

Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.