mlsanityData to 7 Oct 2026

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

arXiv 2201.11000 ↗First posted 26 Jan 2022eess.IV cs.CVcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 20 of its 46 references that are in the map. First posted on arXiv 26 Jan 2022.

Builds on

20 of 46 references.

References · 20

  1. 2017Mask R-CNNcited by 7,259
  2. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  3. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  4. 2015Spatial Transformer Networkscited by 1,423
  5. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 657
  6. 2019Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfacescited by 186
  7. 2018A Deep Learning Framework for Unsupervised Affine and Deformable Image Registrationcited by 160
  8. 2019Data augmentation using learned transformations for one-shot medical image segmentationcited by 138
  9. 2019Recursive Cascaded Networks for Unsupervised Medical Image Registrationcited by 105
  10. 2019DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentationcited by 66
  11. 2019Image-and-Spatial Transformer Networks for Structure-Guided Image Registrationcited by 36
  12. 2020LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentationcited by 28
8 more
  1. 2020Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Imagescited by 17
  2. 2021A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Datacited by 16
  3. 2020PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentationcited by 14
  4. 2019Recurrent Registration Neural Networks for Deformable Image Registrationcited by 11
  5. 2016High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networkscited by 9
  6. 2021Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registrationcited by 8
  7. 2020A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapycited by 7
  8. 2021Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentationcited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 2.

8 papers.

  1. 2023SADIR: Shape-Aware Diffusion Models for 3D Image Reconstructioncited by 12
  2. 2024Multimodal Data Integration for Precision Oncology: Challenges and Future Directionscited by 10
  3. 2022Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CTcited by 5
  4. 2024ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registrationcited by 4
  5. 2024Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancercited by 2
  6. 2025Modality-Aware Feature Matching in Visual and Vision-Language Applications: A Comprehensive Surveycited by 2
  7. 2024Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentationcited by 1
  8. 2025Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlationcited by 0