# Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation

arXiv: https://arxiv.org/abs/1909.04542 · HTML: https://mlsanity.com/paper/1909.04542/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 14 of 14)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
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- [CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection](https://mlsanity.com/paper/2407.06780/) (2024)
- [Two layer Ensemble of Deep Learning Models for Medical Image Segmentation](https://mlsanity.com/paper/2104.04809/) (2021)
- [Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation](https://mlsanity.com/paper/2107.07985/) (2021)
- [Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation](https://mlsanity.com/paper/2102.08556/) (2021)
- [Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation](https://mlsanity.com/paper/2112.11541/) (2021)
- [Automatic segmentation of Organs at Risk in Head and Neck cancer patients from CT and MRI scans](https://mlsanity.com/paper/2405.10833/) (2024)
- [General Incomplete Multimodal Learning via Dynamic Quality Perception](https://mlsanity.com/paper/2607.06943/) (2026)

## Abstract (arXiv)

Lung tumors, especially those located close to or surrounded by soft tissues like the mediastinum, are difficult to segment due to the low soft tissue contrast on computed tomography images. Magnetic resonance images contain superior soft-tissue contrast information that can be leveraged if both modalities were available for training. Therefore, we developed a cross-modality educed learning approach where MR information that is educed from CT is used to hallucinate MRI and improve CT segmentation. Our approach, called cross-modality educed deep learning segmentation (CMEDL) combines CT and pseudo MR produced from CT by aligning their features to obtain segmentation on CT. Features computed in the last two layers of parallelly trained CT and MR segmentation networks are aligned. We implemented this approach on U-net and dense fully convolutional networks (dense-FCN). Our networks were trained on unrelated cohorts from open-source the Cancer Imaging Archive CT images (N=377), an internal archive T2-weighted MR (N=81), and evaluated using separate validation (N=304) and testing (N=333) CT-delineated tumors. Our approach using both networks were significantly more accurate (U-net P <0.001; denseFCN P <0.001) than CT-only networks and achieved an accuracy (Dice similarity coefficient) of 0.71±0.15 (U-net), 0.74±0.12 (denseFCN) on validation and 0.72±0.14 (U-net), 0.73±0.12 (denseFCN) on the testing sets. Our novel approach demonstrated that educing cross-modality information through learned priors enhances CT segmentation performance