Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy

• URL: http://arxiv.org/abs/2007.03817v2
• Date: Sat, 11 Jul 2020 00:33:14 GMT
• Title: Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy
• Authors: Franco Matzkin, Virginia Newcombe, Susan Stevenson, Aneesh Khetani, Tom Newman, Richard Digby, Andrew Stevens, Ben Glocker, Enzo Ferrante
• Abstract summary: We propose a deep learning based method to reconstruct the skull defect removed during craniectomy performed after TBI. This reconstruction is useful in multiple scenarios, e.g. to support the creation of cranioplasty plates.
• Score: 13.695197074035928
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Decompressive craniectomy (DC) is a common surgical procedure consisting of the removal of a portion of the skull that is performed after incidents such as stroke, traumatic brain injury (TBI) or other events that could result in acute subdural hemorrhage and/or increasing intracranial pressure. In these cases, CT scans are obtained to diagnose and assess injuries, or guide a certain therapy and intervention. We propose a deep learning based method to reconstruct the skull defect removed during DC performed after TBI from post-operative CT images. This reconstruction is useful in multiple scenarios, e.g. to support the creation of cranioplasty plates, accurate measurements of bone flap volume and total intracranial volume, important for studies that aim to relate later atrophy to patient outcome. We propose and compare alternative self-supervised methods where an encoder-decoder convolutional neural network (CNN) estimates the missing bone flap on post-operative CTs. The self-supervised learning strategy only requires images with complete skulls and avoids the need for annotated DC images. For evaluation, we employ real and simulated images with DC, comparing the results with other state-of-the-art approaches. The experiments show that the proposed model outperforms current manual methods, enabling reconstruction even in highly challenging cases where big skull defects have been removed during surgery.

Related papers

• Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation [0.2911706166691895]
  The skull segmentation from CT scans can be seen as an already solved problem. In MR this task has a significantly greater complexity due to the presence of soft tissues rather than bones. We propose a fully unsupervised approach, where we do not perform the segmentation directly on MR images, but we rather perform a synthetic CT data generation via MR-to-CT translation.
  arXiv  Detail & Related papers  (2024-10-17T10:51:08Z)
• Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images [10.350643783811174]
  Field-of-view (FOV) recovery of truncated chest CT scans is crucial for accurate body composition analysis. We present a method for recovering truncated CT slices using generative image outpainting. Our model reliably recovers the truncated anatomy and outperforms the previous state-of-the-art despite being trained on 87% less data.
  arXiv  Detail & Related papers  (2024-06-07T09:15:29Z)
• Dual-Task Vision Transformer for Rapid and Accurate Intracerebral Hemorrhage CT Image Classification [9.058538258023383]
  Intracerebral hemorrhage (ICH) is a medical condition caused by the rupture of blood vessels in the brain. We propose a neural network structure, dual-task vision transformer (DTViT) for the automated classification and diagnosis of ICH images.
  arXiv  Detail & Related papers  (2024-05-10T21:17:31Z)
• 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)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models [4.780704816027884]
  We proposed a fully automated prediction of a patient's recanalization score using pre-treatment CT and CTA imaging. Our top model achieved an average cross-validated ROC-AUC of 77.33 $pm$ 3.9%.
  arXiv  Detail & Related papers  (2023-02-08T15:41:21Z)
• 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)
• Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused Ultrasound Interventions with Conditional Adversarial Networks [5.921808547303054]
  Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively under MRI guidance. To accurately target ultrasound through the skull, the transmitted waves must constructively interfere at the target region.
  arXiv  Detail & Related papers  (2022-02-21T11:34:29Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• 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.