# One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs

arXiv: https://arxiv.org/abs/2201.11000 · HTML: https://mlsanity.com/paper/2201.11000/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation](https://mlsanity.com/paper/1904.08465/) (2019)
- [Image-and-Spatial Transformer Networks for Structure-Guided Image Registration](https://mlsanity.com/paper/1907.09200/) (2019)
- [LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation](https://mlsanity.com/paper/2003.07072/) (2020)
- [Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images](https://mlsanity.com/paper/2008.00710/) (2020)
- [A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data](https://mlsanity.com/paper/2110.09260/) (2021)
- [PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation](https://mlsanity.com/paper/2007.09465/) (2020)
- [Recurrent Registration Neural Networks for Deformable Image Registration](https://mlsanity.com/paper/1906.09988/) (2019)
- [High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks](https://mlsanity.com/paper/1604.04339/) (2016)
- [Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration](https://mlsanity.com/paper/2104.07056/) (2021)
- [A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy](https://mlsanity.com/paper/2004.08122/) (2020)
- [Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation](https://mlsanity.com/paper/2102.08556/) (2021)

## Built on by (most cited first, 8 of 8)

- [SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction](https://mlsanity.com/paper/2309.03335/) (2023)
- [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024)
- [Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT](https://mlsanity.com/paper/2210.14297/) (2022)
- [ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration](https://mlsanity.com/paper/2403.16526/) (2024)
- [Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer](https://mlsanity.com/paper/2409.11910/) (2024)
- [Modality-Aware Feature Matching in Visual and Vision-Language Applications: A Comprehensive Survey](https://mlsanity.com/paper/2507.22791/) (2025)
- [Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2408.03616/) (2024)
- [Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlation](https://mlsanity.com/paper/2510.22380/) (2025)

## Abstract (arXiv)

Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because of low soft-tissue contrast, imaging artifacts, respiratory motion, and large treatment induced intra-thoracic anatomic changes. Hence, we developed a novel Patient-specific Anatomic Context and Shape prior or PACS-aware 3D recurrent registration-segmentation network for longitudinal thoracic CBCT segmentation. Segmentation and registration networks were concurrently trained in an end-to-end framework and implemented with convolutional long-short term memory models. The registration network was trained in an unsupervised manner using pairs of planning CT (pCT) and CBCT images and produced a progressively deformed sequence of images. The segmentation network was optimized in a one-shot setting by combining progressively deformed pCT (anatomic context) and pCT delineations (shape context) with CBCT images. Our method, one-shot PACS was significantly more accurate (p<0.001) for tumor (DSC of 0.83 ± 0.08, surface DSC [sDSC] of 0.97 ± 0.06, and Hausdorff distance at 95th percentile [HD95] of 3.97±3.02mm) and the esophagus (DSC of 0.78 ± 0.13, sDSC of 0.90±0.14, HD95 of 3.22±2.02) segmentation than multiple methods. Ablation tests and comparative experiments were also done.