Pretrained hybrid transformer for generalizable cardiac substructures segmentation from contrast and non-contrast CTs in lung and breast cancers
Journal:
arXiv
Published Date:
May 16, 2025
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.
