Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917784577900544 |
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| author | Brioso, Ricardo Coimbra Dei, Damiano Lambri, Nicola Mancosu, Pietro Scorsetti, Marta Loiacono, Daniele |
| author_facet | Brioso, Ricardo Coimbra Dei, Damiano Lambri, Nicola Mancosu, Pietro Scorsetti, Marta Loiacono, Daniele |
| contents | Radiotherapy requires precise segmentation of organs at risk (OARs) and of the Clinical Target Volume (CTV) to maximize treatment efficacy and minimize toxicity. While deep learning (DL) has significantly advanced automatic contouring, complex targets like CTVs remain challenging. This study explores the use of simpler, well-segmented structures (e.g., OARs) as Anatomical Prior (AP) information to improve CTV segmentation. We investigate gender bias in segmentation models and the mitigation effect of the prior information. Findings indicate that incorporating prior knowledge with the discussed strategies enhances segmentation quality in female patients and reduces gender bias, particularly in the abdomen region. This research provides a comparative analysis of new encoding strategies and highlights the potential of using AP to achieve fairer segmentation outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15888 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors Brioso, Ricardo Coimbra Dei, Damiano Lambri, Nicola Mancosu, Pietro Scorsetti, Marta Loiacono, Daniele Image and Video Processing Computer Vision and Pattern Recognition Radiotherapy requires precise segmentation of organs at risk (OARs) and of the Clinical Target Volume (CTV) to maximize treatment efficacy and minimize toxicity. While deep learning (DL) has significantly advanced automatic contouring, complex targets like CTVs remain challenging. This study explores the use of simpler, well-segmented structures (e.g., OARs) as Anatomical Prior (AP) information to improve CTV segmentation. We investigate gender bias in segmentation models and the mitigation effect of the prior information. Findings indicate that incorporating prior knowledge with the discussed strategies enhances segmentation quality in female patients and reduces gender bias, particularly in the abdomen region. This research provides a comparative analysis of new encoding strategies and highlights the potential of using AP to achieve fairer segmentation outcomes. |
| title | Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.15888 |