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Advanced AI Tools to Improve Cancer Diagnosis, Treatment & Monitoring from Multi-Modality Imaging

OncoAI - LymphNode OncoAI - BrainTumor OncoAI - LungNodule

OncoAI - LymphNode

Effortlessly detect, segment, and track lymph nodes across the body with our AI-driven algorithms.

Study Support

  • CT, T1 MRI, and T2 MRI modalities are supported.
  • Studies can be uploaded locally or retrieved from PACS via DICOM node connectivity.

Automatic/Semi-Automatic Localization

  • Automatic localization uses built-in algorithms to identify lymph nodes without any hassle.
  • Semi-automatic localization utilizes user-identified nodes that are created with one-click inputs.

Lymph Node Segmentation & Measurements

  • Lymph node segmentation occurs across the entire study following localization.
  • Automatic quantitative & RECIST measurements are collected upon completion of segmentation.

Report Generation

  • Reports generated show measurement and annotation results.
  • Export reports as a PDF file or push to a specified DICOM node.

Study Comparison

  • Apply longitudinal analysis across series to track lymph nodes over time across all supported modalities.
Study Support
Multi-modality studies can be locally or remotely uploaded to OncoAI - LymphNode.
Automatic or Semi-Automatic Localization
Choose between fully automatic or one-click input lymph node localization.
Lymph Node Segmentation & Measurements
Entire study is segmented and insightful measurements are automatically collected.
Report Generation
Automatically generate reports based on defined segmentations.
Study Comparison
Track lymph node changes over time, using a side-by-side comparison viewer.

OncoAI - BrainTumor

Leverage our award-winning segmentation algorithms for automatic detection, segmentation, and longitudinal tracking of brain tumors.

Parsing, Alignment & Registration

  • Raw MRI data is uploaded or retrieved.
  • The data is automatically parsed & aligned using a robust registration algorithm to reduce motion artifacts.
  • Automatic support for missing modalities.

Multi-Contrast Support

  • Multi-contrast MRI support includes T1, T1 contrast-enhanced, T2, and fluid-attenuated inversion recovery (FLAIR).
  • Flexible and congruent input configurations.

Automatic Localization

  • Automatic localization uses built-in algorithms to locate tumor segmentations in minutes.

Brain Tumor Segmentation

  • Brain tumor segmentation occurs following automatic localization.
  • Precisely detects & quantifies multiple tumor types, such as glioma, brain metastasis, & vestibular schwannoma.
  • Quantitative measurements are collected after segmentation.

Report Generation

  • Reports generated show measurement and annotation results.
  • Export reports as a PDF file or push to a specified DICOM node.

Study Comparison

  • Apply longitudinal analysis across series to track changes over time per tumor ROI.
Parsing, Alignment & Registration
Automatic study parsing to find T1/T2/FLAIR sequences & accurate registration.
Multi-Contrast Support
Multi-contrast MRI support with flexible and congruent input configurations.
Automatic Localization
Automatic localization to locate tumor segmentations in minutes.
Brain Tumor Segmentation
Automatically detect & quantify tumor type with precise quantitative measurements.
Report Generation
Automatically generate reports based on defined segmentations.
Study Comparison
Track tumor changes over time using a side-by-side comparison viewer.

OncoAI - LungNodule

Effortlessly detect, segment, and track lung nodules within CT studies.

Study Support

  • CT modality studies are supported.
  • Studies are uploaded locally or retrieved from PACS via DICOM node connectivity.

Automatic/Semi-Automatic Localization

  • Automatic localization uses built-in algorithms to identify lymph nodes without any hassle.
  • Semi-automatic localization utilizes user-identified nodules that are created with one-click inputs.

Lung Nodule Segmentation & Measurements

  • Lung nodule segmentations occur following localization.
  • Automatic quantitative measurements are collected after segmentation.

Report Generation

  • Reports generated display measurement and annotation results.
  • Export reports as a PDF file or push to a specified DICOM node.

Study Comparison

  • Apply longitudinal analysis across series to track nodules over time.
Study Support
Upload local studies or retrieve remotely via DICOM node connectivity.
Automatic or Semi-Automatic Localization
Choose between fully automatic or one-click input lung nodule localization.
Lung Nodule Segmentation & Measurements
Entire study is segmented and insightful measurements are automatically collected.
Report Generation
Automatically generate reports based on defined segmentations.
Study Comparison
Track lymph node changes over time using a side-by-side comparison viewer.

‍Publications

Brain Tumor Segmentation for Multi-Modal MRI with Missing Information

Xue Feng, Kanchan Ghimire, Daniel D. Kim, Rajat S. Chandra, Helen Zhang, Jian Peng, Binghong Han, Gaofeng Huang, Quan Chen, Sohil Patel, Chetan Bettagowda, Haris I. Sair, Craig Jones, Zhicheng Jiao, Li Yang, Harrison Bai

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Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

Wen Chen, Yimin Li, Brandon A. Dyer, Xue Feng, Shyam Rao, Stanley H. Benedict, Quan Chen, Yi Rong

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Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features

Xue Feng, Nicholas J. Tustison, Sohil H. Patel, Craig H. Meyer

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Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach

L. Zhao, J. D. Asis-Cruz, X. Feng, Y. Wu, K. Kapse, A. Largent, J. Quistorff, C. Lopez, D. Wu, K. Qing, C. Meyer, C. Limperopoulos

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Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge

Yoganand Balagurunathan, Andrew Beers, Michael Mcnitt-Gray, Lubomir Hadjiiski, Sandy Napel, Dmitry Goldgof, Gustavo Perez, Pablo Arbelaez, Alireza Mehrtash, Tina Kapur, Ehwa Yang, Jung Won Moon, Gabriel Bernardino Perez, Ricard Delgado-Gonzalo, M Mehdi Farhangi, Amir A Amini, Renkun Ni, Xue Feng, Aditya Bagari, Kiran Vaidhya, Benjamin Veasey, Wiem Safta, Hichem Frigui, Joseph Enguehard, Ali Gholipour, Laura Silvana Castillo, Laura Alexandra Daza, Paul Pinsky, Jayashree Kalpathy-Cramer, Keyvan Farahani

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Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Brian Huang, John Sollee, Yong-Heng Luo, Ashwin Reddy, Zhusi Zhong, Jing Wu, Joseph Mammarappallil, Terrance Healey, Gang Cheng, Christopher Azzoli, Dana Korogodsky, Paul Zhang, Xue Feng, Jie Li, Li Yang, Zhicheng Jiao, Harrison Xiao Bai

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