7 research outputs found
Automated Identification of Failure Cases in Organ at Risk Segmentation Using Distance Metrics: A Study on CT Data
Automated organ at risk (OAR) segmentation is crucial for radiation therapy
planning in CT scans, but the generated contours by automated models can be
inaccurate, potentially leading to treatment planning issues. The reasons for
these inaccuracies could be varied, such as unclear organ boundaries or
inaccurate ground truth due to annotation errors. To improve the model's
performance, it is necessary to identify these failure cases during the
training process and to correct them with some potential post-processing
techniques. However, this process can be time-consuming, as traditionally it
requires manual inspection of the predicted output. This paper proposes a
method to automatically identify failure cases by setting a threshold for the
combination of Dice and Hausdorff distances. This approach reduces the
time-consuming task of visually inspecting predicted outputs, allowing for
faster identification of failure case candidates. The method was evaluated on
20 cases of six different organs in CT images from clinical expert curated
datasets. By setting the thresholds for the Dice and Hausdorff distances, the
study was able to differentiate between various states of failure cases and
evaluate over 12 cases visually. This thresholding approach could be extended
to other organs, leading to faster identification of failure cases and thereby
improving the quality of radiation therapy planning.Comment: 11 pages, 5 figures, 2 table
Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning
Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning
Introduction: The excellent soft-tissue contrast of magnetic resonance imaging (MRI) is appealing for delineation of organs-at-risk (OARs) as it is required for radiation therapy planning (RTP). In the last decade there has been an increasing interest in using deep-learning (DL) techniques to shorten the labor-intensive manual work and increase reproducibility. This paper focuses on the automatic segmentation of 27 head-and-neck and 10 male pelvis OARs with deep-learning methods based on T2-weighted MR images.Method: The proposed method uses 2D U-Nets for localization and 3D U-Net for segmentation of the various structures. The models were trained using public and private datasets and evaluated on private datasets only.Results and discussion: Evaluation with ground-truth contours demonstrated that the proposed method can accurately segment the majority of OARs and indicated similar or superior performance to state-of-the-art models. Furthermore, the auto-contours were visually rated by clinicians using Likert score and on average, 81% of them was found clinically acceptable
Subventricular Zone Volumetric and Dosimetric Changes during Postoperative Brain Tumor Irradiation and its Impact on Overall Survival
Magnetic Resonance Imaging–Based Delineation of Organs at Risk in the Head and Neck Region
Purpose: The aim of this article is to establish a comprehensive contouring guideline for treatment planning using only magnetic resonance images through an up-to-date set of organs at risk (OARs), recommended organ boundaries, and relevant suggestions for the magnetic resonance imaging (MRI)–based delineation of OARs in the head and neck (H&N) region. Methods and Materials: After a detailed review of the literature, MRI data were collected from the H&N region of healthy volunteers. OARs were delineated in the axial, coronal, and sagittal planes on T2-weighted sequences. Every contour defined was revised by 4 radiation oncologists and subsequently by 2 independent senior experts (H&N radiation oncologist and radiologist). After revision, the final structures were presented to the consortium partners. Results: A definitive consensus was reached after multi-institutional review. On that basis, we provided a detailed anatomic and functional description and specific MRI characteristics of the OARs. Conclusions: In the era of precision radiation therapy, the need for well-built, straightforward contouring guidelines is on the rise. Precise, uniform, delineation-based, automated OAR segmentation on MRI may lead to increased accuracy in terms of organ boundaries and analysis of dose-dependent sequelae for an adequate definition of normal tissue complication probability