Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds

• URL: http://arxiv.org/abs/2503.08515v1
• Date: Tue, 11 Mar 2025 15:07:16 GMT
• Title: Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds
• Authors: David Vallmanya Poch, Yorick Estievenart, Elnura Zhalieva, Sukanya Patra, Mohammad Yaqub, Souhaib Ben Taieb,
• Abstract summary: Cone Beam CT (CBCT) is used throughout Adaptive Radiotherapy (ART) to generate sCTs for improved dose calculations.<n>CBCT suffers from severe artefacts and poor image quality, making it unsuitable for precise dosimetry.<n>Deep learning-based CBCT-to-CT translation has emerged as a promising approach.<n>We propose STF-RUE, a novel framework integrating two key components.
• Score: 2.424469485586727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate dose calculations in proton therapy rely on high-quality CT images. While planning CTs (pCTs) serve as a reference for dosimetric planning, Cone Beam CT (CBCT) is used throughout Adaptive Radiotherapy (ART) to generate sCTs for improved dose calculations. Despite its lower cost and reduced radiation exposure advantages, CBCT suffers from severe artefacts and poor image quality, making it unsuitable for precise dosimetry. Deep learning-based CBCT-to-CT translation has emerged as a promising approach. Still, existing methods often introduce anatomical inconsistencies and lack reliable uncertainty estimates, limiting their clinical adoption. To bridge this gap, we propose STF-RUE, a novel framework integrating two key components. First, STF, a segmentation-guided CBCT-to-CT translation method that enhances anatomical consistency by leveraging segmentation priors extracted from pCTs. Second, RUE, a conformal prediction method that augments predicted CTs with pixel-wise conformal prediction intervals, providing clinicians with robust reliability indicator. Comprehensive experiments using UNet++ and Fast-DDPM on two benchmark datasets demonstrate that STF-RUE significantly improves translation accuracy, as measured by a novel soft-tissue-focused metric designed for precise dose computation. Additionally, STF-RUE provides better-calibrated uncertainty sets for synthetic CT, reinforcing trust in synthetic CTs. By addressing both anatomical fidelity and uncertainty quantification, STF-RUE marks a crucial step toward safer and more effective adaptive proton therapy. Code is available at https://anonymous.4open.science/r/cbct2ct_translation-B2D9/.

Related papers

• ARTInp: CBCT-to-CT Image Inpainting and Image Translation in Radiotherapy [1.70645147263353]
  ARTInp is a novel deep-learning framework combining image inpainting and CBCT-to-CT translation.<n>We trained ARTInp on a dataset of paired CBCT and CT images from the SynthRad 2023 challenge.
  arXiv  Detail & Related papers  (2025-02-07T13:04:25Z)
• Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings [6.157230849293829]
  Daily cone-beam CT (CBCT) imaging, pivotal for therapy adjustment, falls short in tissue density accuracy. We maximize CBCT data during therapy, complemented by sparse paired fan-beam CTs. Our approach shows promise in generating high-quality CT images from CBCT scans in RT.
  arXiv  Detail & Related papers  (2024-09-19T07:56:06Z)
• Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation [0.21847754147782888]
  Cone-beam computed tomography (CBCT) is an important tool facilitating computer aided interventions. While the degraded image quality can affect downstream segmentation, the availability of high quality, preoperative scans represents potential for improvements. We propose a multimodal learning method that fuses roughly aligned CBCT and CT scans and investigate the effect of CBCT quality and misalignment on the final segmentation performance.
  arXiv  Detail & Related papers  (2024-06-17T15:31:54Z)
• WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising [74.14134385961775]
  We introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)
  arXiv  Detail & Related papers  (2024-03-18T11:20:11Z)
• Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers [3.4748713192043876]
  Computed tomography from a low radiation dose (LDCT) is challenging due to high noise in the projection data.<n>We propose a method combining the simplicity and efficiency of two-stage methods with state-of-the-art reconstruction quality.
  arXiv  Detail & Related papers  (2024-03-06T08:51:09Z)
• A multi-channel cycleGAN for CBCT to CT synthesis [0.0]
  Image synthesis is used to generate synthetic CTs from on-treatment cone-beam CTs (CBCTs) Our contribution focuses on the second task, CBCT-to-sCT synthesis. By leveraging a multi-channel input to emphasize specific image features, our approach effectively addresses some of the challenges inherent in CBCT imaging.
  arXiv  Detail & Related papers  (2023-12-04T16:40:53Z)
• Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification [42.75911994044675]
  We present a novel approach for unpaired image-to-image translation of prostate MRIs and an uncertainty-aware training approach for classifying clinically significant PCa. Our approach involves a novel pipeline for translating unpaired 3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available training data. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work.
  arXiv  Detail & Related papers  (2023-07-02T05:26:54Z)
• CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation [56.622832383316215]
  We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans. Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our empirical results show that CyTran outperforms all competing methods.
  arXiv  Detail & Related papers  (2021-10-12T23:25:03Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images [49.1861463923357]
  We propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images. Our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins.
  arXiv  Detail & Related papers  (2021-04-07T16:23:35Z)
• Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation [4.3971310109651665]
  In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment and for deriving biomarkers for treatment response.
  arXiv  Detail & Related papers  (2021-03-09T19:51:44Z)
• COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing [68.68882022019272]
  COVI-AgentSim is an agent-based compartmental simulator based on virology, disease progression, social contact networks, and mobility patterns. We use COVI-AgentSim to perform cost-adjusted analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features.
  arXiv  Detail & Related papers  (2020-10-30T00:47:01Z)
• Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble [77.5625174267105]
  Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population. Multiple phases provide more information than single phase, but they are unaligned and inhomogeneous in texture. We suggest an ensemble of all these alignments as a promising way to boost the performance of PDAC detection.
  arXiv  Detail & Related papers  (2020-03-18T19:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.