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[2602.13299] KidMesh: Computational Mesh Reconstruction for Pediatric Congenital Hydronephrosis Using Deep Neural Networks
Summary
The paper presents KidMesh, a deep learning approach for reconstructing computational meshes for pediatric congenital hydronephrosis from MRU images, enhancing diagnostic capabilities.
Why It Matters
This research addresses a significant gap in pediatric healthcare by improving the accuracy and efficiency of mesh reconstruction from medical imaging, which can lead to better diagnosis and treatment planning for hydronephrosis. The method's ability to operate without precise mesh annotations makes it particularly valuable in clinical settings where such data is scarce.
Key Takeaways
- KidMesh utilizes deep neural networks for efficient mesh reconstruction from MRU images.
- The method eliminates the need for complex post-processing, streamlining workflows.
- Experimental results demonstrate high accuracy with minimal vertex error distances.
- KidMesh can facilitate urodynamic simulations, enhancing clinical assessments.
- The approach is particularly beneficial in pediatric cases where accurate mesh annotations are difficult to obtain.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13299 (cs) [Submitted on 9 Feb 2026] Title:KidMesh: Computational Mesh Reconstruction for Pediatric Congenital Hydronephrosis Using Deep Neural Networks Authors:Haoran Sun, Zhanpeng Zhu, Anguo Zhang, Bo Liu, Zhaohua Lin, Liqin Huang, Mingjing Yang, Lei Liu, Shan Lin, Wangbin Ding View a PDF of the paper titled KidMesh: Computational Mesh Reconstruction for Pediatric Congenital Hydronephrosis Using Deep Neural Networks, by Haoran Sun and 9 other authors View PDF HTML (experimental) Abstract:Pediatric congenital hydronephrosis (CH) is a common urinary tract disorder, primarily caused by obstruction at the renal pelvis-ureter junction. Magnetic resonance urography (MRU) can visualize hydronephrosis, including renal pelvis and calyces, by utilizing the natural contrast provided by water. Existing voxel-based segmentation approaches can extract CH regions from MRU, facilitating disease diagnosis and prognosis. However, these segmentation methods predominantly focus on morphological features, such as size, shape, and structure. To enable functional assessments, such as urodynamic simulations, external complex post-processing steps are required to convert these results into mesh-level representations. To address this limitation, we propose an end-to-end method based on deep neural networks, namely KidMesh, which could automatically reconstruct CH meshes directly from MRU. Generally, KidMesh extracts feature map...
