Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck: Baseline Results

Podobnik, Gašper; Ibragimov, Bulat; Strojan, Primož; Peterlin, Primož; Vrtovec, Tomaž

doi:10.1109/isbi52829.2022.9761433

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…By understanding such a perspective, researchers can tailor their approaches to exploit the strengths of each modality, and improve the accuracy, consistency and quality of deep learning auto-segmentation. 15,16 The baseline auto-segmentation experiments and results, performed and obtained for the images used in this study, 53 indicate that there is still room for improvements that can be leveraged by applying custom solutions, for example, tailored CT and MR modality feature fusion module techniques. 54 Our study is not without limitations.…”

Section: Implications For Auto-segmentationmentioning

confidence: 91%

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

Podobnik,

Ibragimov,

Peterlin

et al. 2024

Medical Physics

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BackgroundAccurate and consistent contouring of organs‐at‐risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR‐only RT, and MR‐guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated.PurposeTo analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the same patients.MethodsIn the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board‐certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95‐percentile Hausdorff distance (HD95).ResultsThe mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1 mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3 mm in terms of DC and HD95, respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5 mm, which was considered as valid for the analysis of intermodality variability.ConclusionsThe contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR‐only and MR‐guided RT.

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Section: Implications For Auto-segmentationmentioning

confidence: 91%

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

Podobnik,

Ibragimov,

Peterlin

et al. 2024

Medical Physics

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Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation

Liu,

Qu,

Xie

et al. 2024

BioMed Eng OnLine

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Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords “multi-organ segmentation” and “deep learning”, resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.

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Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck: Baseline Results

Cited by 2 publications

References 13 publications

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation

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