Automatic segmentation of lung findings in CT and application to Long COVID

• URL: http://arxiv.org/abs/2310.09446v1
• Date: Fri, 13 Oct 2023 23:42:43 GMT
• Title: Automatic segmentation of lung findings in CT and application to Long COVID
• Authors: Diedre S. Carmo, Rosarie A. Tudas, Alejandro P. Comellas, Leticia Rittner, Roberto A. Lotufo, Joseph M. Reinhardt, Sarah E. Gerard
• Abstract summary: S-MEDSeg is a deep learning based approach for accurate segmentation of lung lesions in chest CT images. S-MEDSeg combines a pre-trained EfficientNet backbone, bidirectional feature pyramid network, and modern network advancements.
• Score: 38.69538648742266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated segmentation of lung abnormalities in computed tomography is an important step for diagnosing and characterizing lung disease. In this work, we improve upon a previous method and propose S-MEDSeg, a deep learning based approach for accurate segmentation of lung lesions in chest CT images. S-MEDSeg combines a pre-trained EfficientNet backbone, bidirectional feature pyramid network, and modern network advancements to achieve improved segmentation performance. A comprehensive ablation study was performed to evaluate the contribution of the proposed network modifications. The results demonstrate modifications introduced in S-MEDSeg significantly improves segmentation performance compared to the baseline approach. The proposed method is applied to an independent dataset of long COVID inpatients to study the effect of post-acute infection vaccination on extent of lung findings. Open-source code, graphical user interface and pip package are available at https://github.com/MICLab-Unicamp/medseg.

Related papers

• Shape-aware synthesis of pathological lung CT scans using CycleGAN for enhanced semi-supervised lung segmentation [0.0]
  This paper emphasizes the use of CycleGAN for unpaired image-to-image translation. It provides an augmentation method able to generate fake pathological images matching an existing ground truth. Preliminary results from this research demonstrate significant qualitative and quantitative improvements.
  arXiv  Detail & Related papers  (2024-05-14T12:45:49Z)
• MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography [37.119000111386924]
  MEDPSeg learns from heterogeneous chest CT targets through hierarchical polymorphic multitask learning (HPML) We show PML enabling new state-of-the-art performance for GGO and consolidation segmentation tasks. In addition, MEDPSeg simultaneously performs segmentation of the lung parenchyma, airways, pulmonary artery, and lung lesions, all in a single forward prediction.
  arXiv  Detail & Related papers  (2023-12-04T21:46:39Z)
• MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan [6.266053305874546]
  We propose an efficient end-to-end framework, the multi-encoder-based self-adaptive hard attention network (MESAHA-Net) for precise lung nodule segmentation in CT scans. MESAHA-Net iteratively performs slice-by-slice 2D segmentation of lung nodules, focusing on the nodule region in each slice to generate 3D segmentation of lung nodules. The proposed framework has been evaluated on the LIDC-IDRI dataset, the largest publicly available dataset for lung nodule segmentation.
  arXiv  Detail & Related papers  (2023-04-04T07:05:15Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study [3.083972552471178]
  Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases. Current deep learning models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. The shape of the lungs is reported to be significantly different across the developmental stages from infancy to adulthood.
  arXiv  Detail & Related papers  (2022-11-04T14:06:51Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression from Chest CT Images [59.622239796473885]
  Pulmonary fibrosis is a chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and no known cure. We introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images.
  arXiv  Detail & Related papers  (2021-03-06T02:16:41Z)
• U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation [0.0]
  This article proposes U-Det, a resource-efficient model architecture, which is an end to end deep learning approach to solve the task at hand. The proposed model is extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. The U-Det architecture outperforms the existing U-Net model with the Dice similarity coefficient (DSC) of 82.82% and achieves results comparable to human experts.
  arXiv  Detail & Related papers  (2020-03-20T14:25:22Z)

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.