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

• URL: http://arxiv.org/abs/2201.11000v1
• Date: Wed, 26 Jan 2022 15:18:30 GMT
• Title: One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs
• Authors: Jue Jiang, Harini Veeraraghavan
• Abstract summary: Thoracic CBCTs are hard to segment because of low-tissue contrast, imaging artifacts, respiratory motion, and large treatment induced intra-thoracic anatomic changes. We developed a novel Patient-specific Anatomic Context and prior Shape or PACS- 3D recurrent registration-segmentation network for longitudinal CBCT segmentation.
• Score: 3.3504365823045044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\pm$ 0.08, surface DSC [sDSC] of 0.97 $\pm$ 0.06, and Hausdorff distance at $95^{th}$ percentile [HD95] of 3.97$\pm$3.02mm) and the esophagus (DSC of 0.78 $\pm$ 0.13, sDSC of 0.90$\pm$0.14, HD95 of 3.22$\pm$2.02) segmentation than multiple methods. Ablation tests and comparative experiments were also done.

Related papers

• Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning [0.0]
  We develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI. The method requires the user to click six points near the tumor's extreme boundaries to serve as input for a Convolutional Neural Network.
  arXiv  Detail & Related papers  (2024-02-12T16:15:28Z)
• Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph [49.11220791279602]
  It is challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications. A graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup.
  arXiv  Detail & Related papers  (2023-07-07T18:57:21Z)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images [0.0]
  Tumor segmentation is a fundamental step for radiotherapy treatment planning. This study proposes a novel automatic deep learning (DL) model to assist radiation oncologists in a slice-by-slice GTVp segmentation.
  arXiv  Detail & Related papers  (2022-07-04T15:17:44Z)
• 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)
• Unpaired cross-modality educed distillation (CMEDL) applied to CT lung tumor segmentation [4.409836695738518]
  We develop a new crossmodality educed distillation (CMEDL) approach, using unpaired CT and MRI scans. Our framework uses an end-to-end trained unpaired I2I translation, teacher, and student segmentation networks.
  arXiv  Detail & Related papers  (2021-07-16T15:58:15Z)
• 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)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT [2.9023633922848586]
  We implement and evaluate a multiple resolution residual network (MRRN) for multiple normal organs-at-risk (OAR) segmentation from computed tomography (CT) images. Our approach simultaneously combines feature streams computed at multiple image resolutions and feature levels through residual connections. We trained our approach using 206 thoracic CT scans of lung cancer patients with 35 scans held out for validation to segment the left and right lungs, heart, esophagus, and spinal cord.
  arXiv  Detail & Related papers  (2020-05-27T22:39:09Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)

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.