# LungMend: A Healthy-Only Latent Diffusion Shape Prior for Lung Mask Correction in Severe Tuberculosis CT

**Authors:** Rezkellah Noureddine Khiat, Pierre-Yves Brillet, Catalin Fetita  
**Lab:** Institut Polytechnique de Paris · **Published:** 4 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.06556) · [PDF](https://arxiv.org/pdf/2611.06556) · [Code](https://github.com/noureddinekhiati/LungMend) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.06556.html)

## Abstract

Lung segmentation models such as R231 are widely used, but they fail on severe tuberculosis (TB) CT: cavities and consolidations look like non-lung tissue and are left out of the mask. We present LungMend, a post-processing model that corrects such masks using only healthy anatomy. A latent diffusion model, trained on healthy lungs with synthetically corrupted masks, learns to recover the complete lung from the faulty mask and the bone anatomy, without any pathological data or CT intensities. LungMend recovers cavities and consolidations that R231 misses and improves accuracy on expert-annotated slices. Code: https://github.com/noureddinekhiati/LungMend

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