Multiple Organ Localization and Segmentation for Automatic Treatment Planning



A software for automatic detecting and segmenting tumors and Organs at Risk (OAR) in medical images.

Key Benefits

  • Automatically detect and segment tumors and OARs.
  • Can detect multiple organs and generate their contour maps.

Market Summary

Radiation therapy (RT) is one of the most common treatment modalities for cancer and is used in over half of all treatment plans. Over the past decade, the combination of 3D imaging and RT has enabled the precise delivery of radiation to the tumor while sparing healthy tissues (e.g., lung, intestines, and spinal cord). Computer-aided tomography (CT) and X-rays create a 3D map of the tumor and healthy tissues, allowing the oncologist to deliver radiation to the cancer. Cone-beam Computed Tomography (CBCT) is an imaging method used to obtain 3D images of tissues during daily patient treatment. CBCT is inexpensive, accurate, and uses a lower radiation dose than traditional CT but produces lower contrast images. Hence, new technologies are needed to improve the CBCT contrast, leading to superior treatment outcomes and less radiation-induced toxicity.

Technical Summary

Researchers have developed a method for automatic detecting and segmentation of tumors and OARs (organs at risk) of cancer patient found in medical image (CT/CBCT,MRI, PET/CT and PET/MRI). The method consists of a training stage and segmentation stage. For each pair of medical image and corresponding manual multi-organ contours the contours are to be used as the learning-based target of the medical image. This novelty approach was able to produce contour images from medical images that matched those produced with the existing current manual method.

Development Stage

Proof of concept in human head-and-neck cancers.

Patent Information

Tech ID: 19249
Published: 5/18/2022