Pretrained hybrid transformer for generalizable cardiac substructures segmentation from contrast and non-contrast CTs in lung and breast cancers

• URL: http://arxiv.org/abs/2505.10855v1
• Date: Fri, 16 May 2025 04:48:33 GMT
• Title: Pretrained hybrid transformer for generalizable cardiac substructures segmentation from contrast and non-contrast CTs in lung and breast cancers
• Authors: Aneesh Rangnekar, Nikhil Mankuzhy, Jonas Willmann, Chloe Choi, Abraham Wu, Maria Thor, Andreas Rimner, Harini Veeraraghavan,
• Abstract summary: AI automated segmentations for radiation treatment planning (RTP) can deteriorate when applied in clinical cases with different characteristics than training dataset.<n>We refined a pretrained transformer into a hybrid transformer convolutional network (HTN) to segment cardiac substructures lung and breast cancer patients.<n>A HTN demonstrated robustly accurate (geometric and dose metrics) cardiac substructures segmentation from CTs with varying imaging and patient characteristics.
• Score: 3.704003490598663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI automated segmentations for radiation treatment planning (RTP) can deteriorate when applied in clinical cases with different characteristics than training dataset. Hence, we refined a pretrained transformer into a hybrid transformer convolutional network (HTN) to segment cardiac substructures lung and breast cancer patients acquired with varying imaging contrasts and patient scan positions. Cohort I, consisting of 56 contrast-enhanced (CECT) and 124 non-contrast CT (NCCT) scans from patients with non-small cell lung cancers acquired in supine position, was used to create oracle with all 180 training cases and balanced (CECT: 32, NCCT: 32 training) HTN models. Models were evaluated on a held-out validation set of 60 cohort I patients and 66 patients with breast cancer from cohort II acquired in supine (n=45) and prone (n=21) positions. Accuracy was measured using DSC, HD95, and dose metrics. Publicly available TotalSegmentator served as the benchmark. The oracle and balanced models were similarly accurate (DSC Cohort I: 0.80 \pm 0.10 versus 0.81 \pm 0.10; Cohort II: 0.77 \pm 0.13 versus 0.80 \pm 0.12), outperforming TotalSegmentator. The balanced model, using half the training cases as oracle, produced similar dose metrics as manual delineations for all cardiac substructures. This model was robust to CT contrast in 6 out of 8 substructures and patient scan position variations in 5 out of 8 substructures and showed low correlations of accuracy to patient size and age. A HTN demonstrated robustly accurate (geometric and dose metrics) cardiac substructures segmentation from CTs with varying imaging and patient characteristics, one key requirement for clinical use. Moreover, the model combining pretraining with balanced distribution of NCCT and CECT scans was able to provide reliably accurate segmentations under varied conditions with far fewer labeled datasets compared to an oracle model.

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