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Found 22 papers, 3 papers with code
LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes
In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object.
Learning View-Invariant Features for Person Identification in Temporally Synchronized Videos Taken by Wearable Cameras
In this paper, we attempt to utilize 3D human-skeleton sequences to learn a model that can extract view-invariant motion features from optical flows in different views.
Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material Images
Constructing 3D structures from serial section data is a long standing problem in microscopy.
Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion
Gross tumor volume (GTV) segmentation is a critical step in esophageal cancer radiotherapy treatment planning.
Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk
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).
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search
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.
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
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.
Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy
Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features.
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search
Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow.
SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration.
Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT
252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT.
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications.
LViT: Language meets Vision Transformer in Medical Image Segmentation
In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data.
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Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans
In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs.
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans
Deep learning empowers the mainstream medical image segmentation methods.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning
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-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification
Motivated by this observation, we propose a novel end-to-end framework to improve LN detection performance by leveraging their station information.
Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans
In this work, we propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D.
Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer
Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology.
