# Automatic Organ Segmentation Tool for Radiation Treatment Planning of Cancers

> **NIH NIH R44** · CARINA MEDICAL, LLC · 2022 · $49,504

## Abstract

ABSTRACT
As early detection and better treatment have increased cancer patient survival rates, the importance of
protecting normal organs during radiation treatment is drawing more attention, which is critical in reducing long
term toxicity of cancers. To avoid excessively high radiation doses to organs-at-risk (OARs), OARs need to be
correctly segmented from simulation computed tomography (CT) scans during radiation treatment planning to
get an accurate dose distribution. Despite tremendous effort in developing semi- or fully-automatic
segmentation solutions, current automated segmentation software, mostly using the atlas-based methods, has
not yet reached the level of accuracy and robustness required for clinical usage. Therefore, in current practice,
significant manual efforts are still required in the OAR segmentation process. Manual contouring suffers from
inter- and intra-observer variability, as well as institutional variability where different sites adopt distinct
contouring atlases and labeling criteria, thus leading to inaccuracy and variability in OAR segmentation. When
OARs are very close to the treatment target, segmentation errors as small as a few millimeters can have a
statistically significant impact on dosimetry distribution and outcome. In addition, it is also costly and time
consuming as it can take 1-2 hours of a clinicians’ time to segment major thoracic organs due to the large
number of axial slices required. In summary, an accurate and fast process for segmenting OARs in treatment
planning using CT scans is needed for improving patient outcomes and reducing the cost of radiation therapy
of cancers. In recent years, the rapid development of deep learning methods has revolutionized many
computer-vision areas and the adoption of deep learning in medical applications has shown great success.
Based on a deep-learning-based algorithm we developed that achieved better-than-human performance and
ranked 1st in 2017 American Association of Physicist in Medicine Thoracic Auto-segmentation Challenge, an
automatic OAR segmentation product will be developed in this project with the three aims: 1) further improve
the performance and robustness of OAR segmentation algorithms, focusing on addressing the heterogeneity
issue of different clinical environments; 2) further enrich the functionalities and enhance usability of the cloud-
based software product; and 3) perform clinical validation study on the algorithm performance and software
usability at collaborating sites. With this product, the segmentation accuracy can be improved, leading to more
robust treatment plans in protecting normal organs and improved long term patient outcome. The time and cost
of radiation treatment planning can be greatly reduced, contributing to a more affordable cancer treatment and
reduced healthcare burden.

## Key facts

- **NIH application ID:** 10518374
- **Project number:** 3R44CA254844-03S1
- **Recipient organization:** CARINA MEDICAL, LLC
- **Principal Investigator:** Xue Feng
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $49,504
- **Award type:** 3
- **Project period:** 2022-02-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10518374

## Citation

> US National Institutes of Health, RePORTER application 10518374, Automatic Organ Segmentation Tool for Radiation Treatment Planning of Cancers (3R44CA254844-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10518374. Licensed CC0.

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