Multi-Contrast Computed Tomography Atlas of Healthy Pancreas

• URL: http://arxiv.org/abs/2306.01853v1
• Date: Fri, 2 Jun 2023 18:16:21 GMT
• Title: Multi-Contrast Computed Tomography Atlas of Healthy Pancreas
• Authors: Yinchi Zhou, Ho Hin Lee, Yucheng Tang, Xin Yu, Qi Yang, Shunxing Bao, Jeffrey M. Spraggins, Yuankai Huo, and Bennett A. Landman
• Abstract summary: A volumetric spatial reference is needed to adapt the morphological variability for organ-specific analysis. We propose a high-resolution computed tomography (CT) atlas framework specifically optimized for the pancreas organ.
• Score: 20.701287373470425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the substantial diversity in population demographics, such as differences in age and body composition, the volumetric morphology of pancreas varies greatly, resulting in distinctive variations in shape and appearance. Such variations increase the difficulty at generalizing population-wide pancreas features. A volumetric spatial reference is needed to adapt the morphological variability for organ-specific analysis. Here, we proposed a high-resolution computed tomography (CT) atlas framework specifically optimized for the pancreas organ across multi-contrast CT. We introduce a deep learning-based pre-processing technique to extract the abdominal region of interests (ROIs) and leverage a hierarchical registration pipeline to align the pancreas anatomy across populations. Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed high-resolution atlas template. To generate and evaluate the pancreas atlas template, multi-contrast modality CT scans of 443 subjects (without reported history of pancreatic disease, age: 15-50 years old) are processed. Comparing with different registration state-of-the-art tools, the combination of DEEDs affine and non-rigid registration achieves the best performance for the pancreas label transfer across all contrast phases. We further perform external evaluation with another research cohort of 100 de-identified portal venous scans with 13 organs labeled, having the best label transfer performance of 0.504 Dice score in unsupervised setting. The qualitative representation (e.g., average mapping) of each phase creates a clear boundary of pancreas and its distinctive contrast appearance. The deformation surface renderings across scales (e.g., small to large volume) further illustrate the generalizability of the proposed atlas template.

Related papers

• Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization [21.125010099161774]
  Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution. longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices. We propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice.
  arXiv  Detail & Related papers  (2023-09-17T22:53:16Z)
• Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection [1.6539563017720214]
  We propose a new anatomy-informed transformation that leverages information from adjacent organs to simulate physiological deformations of the prostate. We demonstrate the effectiveness of our augmentation on a dataset of 774 biopsy-confirmed examinations.
  arXiv  Detail & Related papers  (2023-09-07T11:46:59Z)
• 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)
• A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology [62.997667081978825]
  Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
  arXiv  Detail & Related papers  (2022-09-27T14:08:19Z)
• Multi-phase Deformable Registration for Time-dependent Abdominal Organ Variations [81.37460333873524]
  We propose a time-efficient and accurate deformable registration algorithm for multi-phase CT scans considering abdominal organ motions. Experimental results show the registration accuracy as 0.85 +/- 0.45mm (mean +/- STD) for pancreas within 1 minute for the whole abdominal region.
  arXiv  Detail & Related papers  (2021-03-08T15:43:23Z)
• Multi-Contrast Computed Tomography Healthy Kidney Atlas [3.0066242826634415]
  A volumetric atlas framework is needed to integrate and visualize the variability across scales. There is no abdominal and retroperitoneal organs atlas framework for multi-contrast CT. We introduce a deep learning-based volume of interest extraction method and an automated two-stage hierarchal registration pipeline to register abdominal volumes to a high-resolution CT atlas template.
  arXiv  Detail & Related papers  (2020-12-23T01:03:16Z)
• Development and Characterization of a Chest CT Atlas [5.770017830153421]
  We propose a thoracic atlas built upon a large low dose CT database of lung cancer screening program. To provide spatial mapping, we optimize a multi-stage inter-subject non-rigid registration pipeline for the entire thoracic space. The application validity of the developed atlas is evaluated in terms of discriminative capability for different anatomic phenotypes.
  arXiv  Detail & Related papers  (2020-12-05T21:20:57Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• 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)
• Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors [72.65802386845002]
  This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs) in abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. Our approach outperforms state-of-the-art segmentation networks and achieves a sensitivity of $89.47%$ at a specificity of $81.08%$.
  arXiv  Detail & Related papers  (2020-04-04T21:21:44Z)

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.