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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2401.07542 |
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| _version_ | 1866911970108637184 |
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| author | Keuth, Ron Heinrich, Mattias |
| author_facet | Keuth, Ron Heinrich, Mattias |
| contents | When solving a segmentation task, shaped-base methods can be beneficial compared to pixelwise classification due to geometric understanding of the target object as shape, preventing the generation of anatomical implausible predictions in particular for corrupted data. In this work, we propose a novel hybrid method that combines a lightweight CNN backbone with a geometric neural network (Point Transformer) for shape regression. Using the same CNN encoder, the Point Transformer reaches segmentation quality on per with current state-of-the-art convolutional decoders ($4\pm1.9$ vs $3.9\pm2.9$ error in mm and $85\pm13$ vs $88\pm10$ Dice), but crucially, is more stable w.r.t image distortion, starting to outperform them at a corruption level of 30%. Furthermore, we include the nnU-Net as an upper baseline, which has $3.7\times$ more trainable parameters than our proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combining Image- and Geometric-based Deep Learning for Shape Regression: A Comparison to Pixel-level Methods for Segmentation in Chest X-Ray Keuth, Ron Heinrich, Mattias Computer Vision and Pattern Recognition When solving a segmentation task, shaped-base methods can be beneficial compared to pixelwise classification due to geometric understanding of the target object as shape, preventing the generation of anatomical implausible predictions in particular for corrupted data. In this work, we propose a novel hybrid method that combines a lightweight CNN backbone with a geometric neural network (Point Transformer) for shape regression. Using the same CNN encoder, the Point Transformer reaches segmentation quality on per with current state-of-the-art convolutional decoders ($4\pm1.9$ vs $3.9\pm2.9$ error in mm and $85\pm13$ vs $88\pm10$ Dice), but crucially, is more stable w.r.t image distortion, starting to outperform them at a corruption level of 30%. Furthermore, we include the nnU-Net as an upper baseline, which has $3.7\times$ more trainable parameters than our proposed method. |
| title | Combining Image- and Geometric-based Deep Learning for Shape Regression: A Comparison to Pixel-level Methods for Segmentation in Chest X-Ray |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.07542 |