Three-dimensional Segmentation of the Scoliotic Spine from MRI using Unsupervised Volume-based MR-CT Synthesis

• URL: http://arxiv.org/abs/2011.14005v1
• Date: Wed, 25 Nov 2020 18:34:52 GMT
• Title: Three-dimensional Segmentation of the Scoliotic Spine from MRI using Unsupervised Volume-based MR-CT Synthesis
• Authors: Enamundram M. V. Naga Karthik, Catherine Laporte, Farida Cheriet
• Abstract summary: We present an unsupervised, fully three-dimensional (3D) cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN model is trained for an unpaired volume-to-volume translation across MR and CT domains. The resulting segmentation is used to reconstruct a 3D model of the spine.
• Score: 3.6273410177512275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from CT images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of paired MR-CT data, which is rare, especially in the case of scoliotic patients. In this paper, we present a completely unsupervised, fully three-dimensional (3D) cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN model is trained for an unpaired volume-to-volume translation across MR and CT domains. Then, the Otsu thresholding algorithm is applied to the synthesized CT volumes for easy segmentation of the vertebral bones. The resulting segmentation is used to reconstruct a 3D model of the spine. We validate our method on 28 scoliotic vertebrae in 3 patients by computing the point-to-surface mean distance between the landmark points for each vertebra obtained from pre-operative X-rays and the surface of the segmented vertebra. Our study results in a mean error of 3.41 $\pm$ 1.06 mm. Based on qualitative and quantitative results, we conclude that our method is able to obtain a good segmentation and 3D reconstruction of scoliotic spines, all after training from unpaired data in an unsupervised manner.

Related papers

• Weakly-Supervised Detection of Bone Lesions in CT [48.34559062736031]
  The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. We developed a pipeline to detect bone lesions in CT volumes via a proxy segmentation task. Our method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data.
  arXiv  Detail & Related papers  (2024-01-31T21:05:34Z)
• 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)
• Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading [70.04389979779195]
  This paper proposes a novel transformer-based model architecture for medical imaging problems involving analysis of vertebrae. It considers two applications of such models in MR images: (a) detection of spinal metastases and the related conditions of vertebral fractures and metastatic cord compression. We show that by considering the context of vertebral bodies in the image, SCT improves the accuracy for several gradings compared to previously published model.
  arXiv  Detail & Related papers  (2022-06-27T10:31:03Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization [0.04925906256430176]
  Vertebral body compression fractures are early signs of osteoporosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images. We then detect individual vertebrae and quantify fractures in 2D simultaneously.
  arXiv  Detail & Related papers  (2022-04-14T08:31:25Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• 3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology [2.4984854046383624]
  We present methodology to reconstruct and segment full brain image volumes from brain dissection photographs. The 3D reconstruction is achieved via a joint registration framework, which uses a reference volume other than MRI. We evaluate our methods on a dataset with 24 brains, using Dice scores and volume correlations.
  arXiv  Detail & Related papers  (2020-09-11T18:21:00Z)
• Deep Negative Volume Segmentation [60.44793799306154]
  We propose a new angle to the 3D segmentation task: segment empty spaces between all the tissues surrounding the object. Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation. We validate the idea on the CT scans in a 50-patient dataset, annotated by experts in maxillofacial medicine.
  arXiv  Detail & Related papers  (2020-06-22T16:55:23Z)
• Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification [0.04925906256430176]
  Vertebral body compression fractures are reliable early signs of osteoporosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images. We simultaneously detect individual vertebrae and quantify fractures in 2D.
  arXiv  Detail & Related papers  (2020-05-25T08:05:27Z)
• A$^3$DSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation [18.500206499468902]
  CBCT images are of low-quality and artifact-laden due to noise, poor tissue contrast, and the presence of metallic objects. There exists a wealth of artifact-free, high quality CT images with vertebra annotations. This motivates us to build a CBCT vertebra segmentation model using unpaired CT images with annotations.
  arXiv  Detail & Related papers  (2020-01-02T06:37:09Z)

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.