Available Segmentations

Head & Neck

Approved Structures

Investigational Use

Mandible
L. Brachial Plexus
R. Brachial Plexus
Brain
Brainstem
Oval Cavity
L. Cochlea
R. Cochlea
L. Eye
R. Eye
L. Lacrimal Gland
R. Lacrimal Gland
L. Submandibular Gland
R. Submandibular Gland
L. Inner Ear
R. Inner Ear
L. Temporomandibular Joint
R. Temporomandibular Joint
L. Lens
R. Lens
L. Temporal Lobe
R. Temporal Lobe

Larynx
L. Middle Ear
R. Middle Ear
Optic Chiasm
L. Optic Nerve
R. Optic Nerve
L. Parotid
R. Parotid
Pharynx
Pituitary

GTV Lymph Node
GTV Primary Tumor
Oral Cavity, NRG Standard
L. Cerebellum
R. Cerebellum
Cervical Spine
Glottis, NRG Standard
L. Neck Lymph Node Level III
R. Neck Lymph Node Level III
L. Neck Lymph Node Level II
R. Neck Lymph Node Level II
L. Neck Lymph Node Level IVa
R. Neck Lymph Node Level IVa
L. Neck Lymph Node Level Ib
R. Neck Lymph Node Level Ib
Larynx, NRG Standard
Larynx Supraglottis, NRG Standard
Lips, NRG Standard
Cricopharyngeal Muscle
Inferior Pharyngeal Constrictor
Middle Pharyngeal Constrictor
Superior Pharyngeal Constrictor

Larynx
L. Middle Ear
R.Middle Ear
Optic Chiasm
L. Optic Nerve
R. Optic Nerve
L. Parotid
R. Parotid
Pharynx
Pituitary

Thorax

Abdomen

Male Pelvis

Female Pelvis

Robust Quality Assurance

INTContour automatically generates quality assurance reports based on user selections to validate segmentation. Calculate Dice Score, Mean Surface Distance, and Hausdorff Distance between AI outputs and ground truth values with a single click.

An example of INTContour's user interface for running a quality assurance check on certain segmentations within a series.

Powerful Workflow Applications with Seamless Connectivity

INTContour operates as a stand-alone web-based system, or integrates with applications via Automatic Workflow or TPS Connectivity. Using Automatic Workflow, INTContour efficiently segments the desired studies and transfers them back to the main application in use.

A graphic illustrating simple workflow options that INTContour can be integrated into.

Research Tools Made for Generating New Insights

INTContour streamlines your research with intuitive features to train models without coding and process large volumes of studies with a single command using award-winning AI algorithms.

An example of INTContour's built-in viewer with automated segmentations visible.

Interactive Study Viewer

Effortlessly refine segmentations directly in your browser before sending them to your Treatment Planning Station.
Use INTContour’s comparison tool to view annotation differences across series within a specific study.

An example of the ROI editing feature available within INTContour's built-in viewer.

Advanced Editing & Analysis

Calculate gross total volume of lymph nodes and primary tumors from PET/CT studies.
INTContour’s Smart Interpolation tracks and interpolates changes to unedited slices in order to reduce annotation time.

A figure illustrating INTContour's custom protocol page.

Segmentation Protocol Customization

Develop new protocols by adjusting regions of interest (ROIs), naming conventions, and morphological operations to match specific needs.
Customize Treatment Planning System (TPS) settings for new protocols to seamlessly align with your workflow preferences.

Incremental Learning

INTContour trains new models using your on-site data.
Optimize your models with a robust backbone network structure in order to support any structures you need. *
Allows for fused multi-modality inputs with registration.

‍* Tools provided as a research-only feature. INTContour does not assure the accuracy and performance of your new models.

A figure illustrating INTContour's Batch Mode segmentation function.

Batch Study Processing

Leverage your workstation to efficiently parse local folders with multiple studies and output specified contours for all data at once.
Enhance image quality using CBCT to CT translation and generate synthetic CT from MRI.
Replace Protected Health Information (PHI) with anonymized values in longitudinal studies while preserving all DICOM-RT data links.

Publications

Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy

Nimu Yuan, Brandon Dyer, Shyam Rao, Quan Chen, Stanley Benedict, Lu Shang, Yan Kang, Jinvi Qi, Yi Rong

Read Here

Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries

Solomon Kibudde, Awusi Kavuma, Yao Hao, Tianyu Zhao, Hiram Gay, Jacaranda Van Rheenen, Pavan Mukesh Jhaveri, Minjmaa Minjgee, Enkhsetseg Vanchinbazar, Urdenekhuu Nansalmaa, Baozhou Sun

Read Here

Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation

Xue Feng, Mark E. Bernard, Thomas Hunter, Quan Chen

Read Here

Automatic segmentation of all lower limb muscles from high-resolution magnetic resonance imaging using a cascaded three-dimensional deep convolutional neural network

Renkun Ni, Craig H. Meyer, Silvia S. Blemker, Joseph M. Hart, Xue Feng

Read Here

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation

Jingwei Duan, Mark E. Bernard, James R. Castle, Xue Feng, Chi Wang, Mark C. Kenamond, Quan Chen

Read Here

Technical note: Atlas-based Auto-segmentation of masticatory muscles for head and neck cancer radiotherapy

Xiangguo Zhang, Haihui Chen, Wen Chen, Brandon A. Dyer, Quan Chen, Stanley H. Benedict, Shyam Rao, Yi Rong

Read Here

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout

Lin-Mei Zhao, Helen Zhang, Daniel D. Kim, Kanchan Ghimire, Rong Hu, Daniel C. Kargilis, Lei Tang, Shujuan Meng, Quan Chen, Wei-Hua Liao, Harrison Bai, Zhicheng Jiao, Xue Feng

Read Here

First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients

Jingwei Duan, Mark Bernard, Laura Downes, Brooke Willows, Xue Feng, Waleed F. Mourad, William St Clair, Quan Chen

Read Here

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process

Jingwei Duan, Mark Bernard, Laura Downes, Brooke Willows, Xue Feng, Waleed F. Mourad, William St Clair, Quan Chen

Read Here

Fully Customizable AI-Powered OAR & Target Segmentation with Automatic Quality Assurance

Available Segmentations

Head & Neck

Approved Structures

Investigational Use

Thorax

Abdomen

Male Pelvis

Female Pelvis

Robust Quality Assurance

Powerful Workflow Applications with Seamless Connectivity

Research Tools Made for Generating New Insights

Interactive Study Viewer

Advanced Editing & Analysis

Segmentation Protocol Customization

Incremental Learning

Batch Study Processing

Publications

Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy

Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries

Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation

Automatic segmentation of all lower limb muscles from high-resolution magnetic resonance imaging using a cascaded three-dimensional deep convolutional neural network

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation

Technical note: Atlas-based Auto-segmentation of masticatory muscles for head and neck cancer radiotherapy

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout

First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process

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