Impact of deep learning model uncertainty on manual corrections to auto-segmentation in prostate cancer radiotherapy
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918139552333824 |
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| author | Rogowski, Viktor Svalkvist, Angelica Maspero, Matteo Janssen, Tomas Maruccio, Federica Carmen Gorgisyan, Jenny Scherman, Jonas Häggström, Ida Wåhlstrand, Victor Gunnlaugsson, Adalsteinn Nilsson, Martin P Moreau, Mathieu Vass, Nándor Pettersson, Niclas Gustafsson, Christian Jamtheim |
| author_facet | Rogowski, Viktor Svalkvist, Angelica Maspero, Matteo Janssen, Tomas Maruccio, Federica Carmen Gorgisyan, Jenny Scherman, Jonas Häggström, Ida Wåhlstrand, Victor Gunnlaugsson, Adalsteinn Nilsson, Martin P Moreau, Mathieu Vass, Nándor Pettersson, Niclas Gustafsson, Christian Jamtheim |
| contents | Background: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy, yet voxel-wise DL uncertainty maps are rarely presented to clinicians. Purpose: This study assessed how DL-generated uncertainty maps impact radiation oncologists during manual correction of prostate radiotherapy DL segmentations. Methods: Two nnUNet models were trained by 10-fold cross-validation on 434 MRI-only prostate cancer cases to segment the prostate and rectum. Each model was evaluated on 35 independent cases. Voxel-wise uncertainty was calculated using the SoftMax standard deviation (n=10) and visualized as a color-coded map. Four oncologists performed segmentation in two steps: Step 1: Rated segmentation quality and confidence using Likert scales and edited DL segmentations without uncertainty maps. Step 2 ($\geq 4$ weeks later): Repeated step 1, but with uncertainty maps available. Segmentation time was recorded for both steps, and oncologists provided qualitative free-text feedback. Histogram analysis compared voxel edits across uncertainty levels. Results: DL segmentations showed high agreement with oncologist edits. Quality ratings varied: rectum segmentation ratings slightly decreased overall in step 2, while prostate ratings differed among oncologists. Confidence ratings also varied. Three oncologists reduced segmentation time with uncertainty maps, saving 1-2 minutes per case. Histogram analysis showed 50% fewer edits for step 2 in low-uncertainty areas. Conclusions: Presenting DL segmentation uncertainty information to radiation oncologists influences their decision-making, quality perception, and confidence in the DL segmentations. Low-uncertainty regions were edited less frequently, indicating increased trust in DL predictions. Uncertainty maps improve efficiency by reducing segmentation time and can be a valuable clinical tool, enhancing radiotherapy planning efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18973 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of deep learning model uncertainty on manual corrections to auto-segmentation in prostate cancer radiotherapy Rogowski, Viktor Svalkvist, Angelica Maspero, Matteo Janssen, Tomas Maruccio, Federica Carmen Gorgisyan, Jenny Scherman, Jonas Häggström, Ida Wåhlstrand, Victor Gunnlaugsson, Adalsteinn Nilsson, Martin P Moreau, Mathieu Vass, Nándor Pettersson, Niclas Gustafsson, Christian Jamtheim Medical Physics Background: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy, yet voxel-wise DL uncertainty maps are rarely presented to clinicians. Purpose: This study assessed how DL-generated uncertainty maps impact radiation oncologists during manual correction of prostate radiotherapy DL segmentations. Methods: Two nnUNet models were trained by 10-fold cross-validation on 434 MRI-only prostate cancer cases to segment the prostate and rectum. Each model was evaluated on 35 independent cases. Voxel-wise uncertainty was calculated using the SoftMax standard deviation (n=10) and visualized as a color-coded map. Four oncologists performed segmentation in two steps: Step 1: Rated segmentation quality and confidence using Likert scales and edited DL segmentations without uncertainty maps. Step 2 ($\geq 4$ weeks later): Repeated step 1, but with uncertainty maps available. Segmentation time was recorded for both steps, and oncologists provided qualitative free-text feedback. Histogram analysis compared voxel edits across uncertainty levels. Results: DL segmentations showed high agreement with oncologist edits. Quality ratings varied: rectum segmentation ratings slightly decreased overall in step 2, while prostate ratings differed among oncologists. Confidence ratings also varied. Three oncologists reduced segmentation time with uncertainty maps, saving 1-2 minutes per case. Histogram analysis showed 50% fewer edits for step 2 in low-uncertainty areas. Conclusions: Presenting DL segmentation uncertainty information to radiation oncologists influences their decision-making, quality perception, and confidence in the DL segmentations. Low-uncertainty regions were edited less frequently, indicating increased trust in DL predictions. Uncertainty maps improve efficiency by reducing segmentation time and can be a valuable clinical tool, enhancing radiotherapy planning efficiency. |
| title | Impact of deep learning model uncertainty on manual corrections to auto-segmentation in prostate cancer radiotherapy |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2502.18973 |