Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment

• URL: http://arxiv.org/abs/2006.06432v1
• Date: Wed, 10 Jun 2020 12:05:55 GMT
• Title: Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment
• Authors: Fahdi Kanavati, Shah Islam, Zohaib Arain, Eric O. Aboagye, Andrea Rockall
• Abstract summary: This retrospective study was conducted using a collection of public and privately available CT images. The method consisted of two stages: slice detection from a CT volume and single-slice CT segmentation. The output consisted of a segmented muscle mass on a CT slice at the level of L3 vertebra.
• Score: 0.10499611180329801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: To demonstrate the effectiveness of using a deep learning-based approach for a fully automated slice-based measurement of muscle mass for assessing sarcopenia on CT scans of the abdomen without any case exclusion criteria. Materials and Methods: This retrospective study was conducted using a collection of public and privately available CT images (n = 1070). The method consisted of two stages: slice detection from a CT volume and single-slice CT segmentation. Both stages used Fully Convolutional Neural Networks (FCNN) and were based on a UNet-like architecture. Input data consisted of CT volumes with a variety of fields of view. The output consisted of a segmented muscle mass on a CT slice at the level of L3 vertebra. The muscle mass is segmented into erector spinae, psoas, and rectus abdominus muscle groups. The output was tested against manual ground-truth segmentation by an expert annotator. Results: 3-fold cross validation was used to evaluate the proposed method. The slice detection cross validation error was 1.41+-5.02 (in slices). The segmentation cross validation Dice overlaps were 0.97+-0.02, 0.95+-0.04, 0.94+-0.04 for erector spinae, psoas, and rectus abdominus, respectively, and 0.96+-0.02 for the combined muscle mass. Conclusion: A deep learning approach to detect CT slices and segment muscle mass to perform slice-based analysis of sarcopenia is an effective and promising approach. The use of FCNN to accurately and efficiently detect a slice in CT volumes with a variety of fields of view, occlusions, and slice thicknesses was demonstrated.

Related papers

• Improved 3D Whole Heart Geometry from Sparse CMR Slices [3.701571763780745]
  Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically acquires multiple sparse 2D slices, with unavoidable respiratory motion artefacts between slices, whereas CT acquires isotropic dense data but uses ionising radiation. We explore the combination of Slice Shifting Algorithm (SSA), Spatial Transformer Network (STN), and Label Transformer Network (LTN) to: 1) correct respiratory motion between segmented slices, and 2) transform sparse segmentation data into dense segmentation.
  arXiv  Detail & Related papers  (2024-08-14T13:03:48Z)
• A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset [1.0318017891096118]
  We train a ResNet-18 network to classify axial slices of lymphoma/CT images. Model performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) We observe and describe a performance overestimation in the case of slice-level split as compared to the patient-level split training.
  arXiv  Detail & Related papers  (2024-03-11T18:57:45Z)
• 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)
• Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases [2.680862925538592]
  Small liver lesions common to colorectal liver (CRLMs) are challenging for convolutional neural network (CNN) segmentation models. We propose an unsupervised attention-based model to generate intermediate slices from consecutive triplet slices in CT scans. Our model's outputs are consistent with the original input slices while increasing the segmentation performance in two cutting-edge 3D segmentation pipelines.
  arXiv  Detail & Related papers  (2023-08-30T10:21:57Z)
• 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)
• Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training [19.86796625044402]
  We propose an unsupervised domain adaptation pipeline with self-training to transfer labels from 3D MR to single CT slice. On 152 withheld single CT thigh images, the proposed pipeline achieved a mean Dice of 0.888(0.041) across all muscle groups.
  arXiv  Detail & Related papers  (2022-11-30T19:04:17Z)
• 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)
• CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images [58.720142291102135]
  We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN) For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
  arXiv  Detail & Related papers  (2021-11-16T15:03:42Z)
• A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images [25.76523855274612]
  A three-dimensional (3D) end-to-end fully convolutional neural network was developed for our segmentation task. Two models with the same network structures were trained and they achieved a dice similarity coefficient (DSC) of 97.87% and 96.49% for the periosteal and endosteal contours, respectively. It demonstrated a strong potential for clinical use, including the hip fracture risk prediction and finite element analysis.
  arXiv  Detail & Related papers  (2021-02-03T10:23:41Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• 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.