arXiv subjects

Chloe Min Seo Choi

Publications and source records attributed to Chloe Min Seo Choi.

3 recordsLinked to original sources

arXiv2026

Transformer-based cardiac substructure segmentation from contrast and non-contrast computed tomography for radiotherapy planning

Accurate segmentation of cardiac substructures on computed tomography (CT) scans is essential for radiotherapy planning but typically requires large annotated datasets and often generalizes poorly across imaging protocols and patient variations. This study evaluated whether pretrained transformers enable data-efficient training using a fixed architecture with balanced curriculum learning. A hybrid pretrained transformer-convolutional network (SMIT) was fine-tuned on lung cancer patients (Cohort I, N $=$ 180) imaged in the supine position and validated on 60 held-out Cohort I patients and 65 breast cancer patients (Cohort II) imaged in both supine and prone positions. Two configurations were evaluated: SMIT-Balanced (32 contrast-enhanced CTs and 32 non-contrast CTs) and SMIT-Oracle (180 CTs). Performance was compared with nnU-Net and TotalSegmentator. Segmentation accuracy was assessed primarily using the 95th percentile Hausdorff distance (HD95), with radiation dose and overlap-based metrics evaluated as secondary endpoints. SMIT-Balanced achieved accuracy comparable to SMIT-Oracle despite using 64$\%$ fewer training scans. On Cohort I, HD95 was 6.6 $\pm$ 4.3 mm versus 5.4 $\pm$ 2.6 mm, and on Cohort II, 10.0 $\pm$ 9.4 mm versus 9.4 $\pm$ 9.8 mm, respectively, demonstrating robustness to patient, imaging, and data variations. Radiation dose metrics derived from SMIT segmentations were equivalent to those from manual delineations. Although nnU-Net improved over the publicly trained TotalSegmentator, it showed reduced cross-domain robustness compared to SMIT. Balanced curriculum training reduced labeled data requirements without compromising accuracy relative to the oracle model and maintained robustness across patient and imaging variations. Pretraining reduced dependence on data domain and obviated the need for data-specific architectural reconfiguration required by nnU-Net.

eess.IV↗

arXiv2024

Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer

Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images. Purpose: We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA. Methods: TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF). Multiple CLSTM steps are used to compute a progressive sequence of deformations. Input conditioning was applied by including tumor segmentations with 3D image pairs as input channels. Bidirectional tumor rigidity, image similarity, and deformation smoothness losses were used to optimize the network in an unsupervised manner. TRACER and multiple DL methods were trained with 204 3D CT image pairs from patients with lung cancers (LC) and evaluated using (a) Dataset I (N = 308 pairs) with DL segmented LCs, (b) Dataset II (N = 765 pairs) with manually delineated LCs, and (c) Dataset III with 42 LC patients treated with RT. Results: TRACER accurately aligned normal tissues. It best preserved tumors, blackindicated by the smallest tumor volume difference of 0.24\%, 0.40\%, and 0.13 \% and mean square error in CT intensities of 0.005, 0.005, 0.004, computed between original and resampled moving image tumors, for Datasets I, II, and III, respectively. It resulted in the smallest planned RT tumor dose difference computed between original and resampled moving images of 0.01 Gy and 0.013 Gy when using a female and a male reference.

eess.IV↗

arXiv2024

Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis

Pretraining vision transformers (ViT) with attention guided masked image modeling (MIM) has shown to increase downstream accuracy for natural image analysis. Hierarchical shifted window (Swin) transformer, often used in medical image analysis cannot use attention guided masking as it lacks an explicit [CLS] token, needed for computing attention maps for selective masking. We thus enhanced Swin with semantic class attention. We developed a co-distilled Swin transformer that combines a noisy momentum updated teacher to guide selective masking for MIM. Our approach called \textsc{s}e\textsc{m}antic \textsc{a}ttention guided co-distillation with noisy teacher \textsc{r}egularized Swin \textsc{T}rans\textsc{F}ormer (SMARTFormer) was applied for analyzing 3D computed tomography datasets with lung nodules and malignant lung cancers (LC). We also analyzed the impact of semantic attention and noisy teacher on pretraining and downstream accuracy. SMARTFormer classified lesions (malignant from benign) with a high accuracy of 0.895 of 1000 nodules, predicted LC treatment response with accuracy of 0.74, and achieved high accuracies even in limited data regimes. Pretraining with semantic attention and noisy teacher improved ability to distinguish semantically meaningful structures such as organs in a unsupervised clustering task and localize abnormal structures like tumors. Code, models will be made available through GitHub upon paper acceptance.

cs.CV↗