Chest X-ray lung and heart segmentation based on minimal training sets

• URL: http://arxiv.org/abs/2101.08309v1
• Date: Wed, 20 Jan 2021 20:24:35 GMT
• Title: Chest X-ray lung and heart segmentation based on minimal training sets
• Abstract summary: We present a deep neural network to which we refer as Attention BCDU-Net, and apply it to the task of lung and heart segmentation from chest X-ray (CXR) images. We show that the fine-tuned model exceeds previous state-of-the-art results, reaching $98.1pm 0.1%$ Dice score and $95.2pm 0.1%$ IoU score.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the COVID-19 pandemic aggravated the excessive workload of doctors globally, the demand for computer aided methods in medical imaging analysis increased even further. Such tools can result in more robust diagnostic pipelines which are less prone to human errors. In our paper, we present a deep neural network to which we refer to as Attention BCDU-Net, and apply it to the task of lung and heart segmentation from chest X-ray (CXR) images, a basic but ardous step in the diagnostic pipeline, for instance for the detection of cardiomegaly. We show that the fine-tuned model exceeds previous state-of-the-art results, reaching $98.1\pm 0.1\%$ Dice score and $95.2\pm 0.1\%$ IoU score on the dataset of Japanese Society of Radiological Technology (JSRT). Besides that, we demonstrate the relative simplicity of the task by attaining surprisingly strong results with training sets of size 10 and 20: in terms of Dice score, $97.0\pm 0.8\%$ and $97.3\pm 0.5$, respectively, while in terms of IoU score, $92.2\pm 1.2\%$ and $93.3\pm 0.4\%$, respectively. To achieve these scores, we capitalize on the mixup augmentation technique, which yields a remarkable gain above $4\%$ IoU score in the size 10 setup.

Related papers

• Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models [0.8590539335179757]
  This research paper proposes utilizing unsupervised Denoising Diffusion Probabilistic Models (DDPMs) to extract anatomical features.<n>We train a DDPM on 21 unlabeled abdominal CT scans to learn structural representations, transferring the encoder weights to a downstream segmentation task.<n>Using unlabeled images for diffusion-based pretraining successfully embeds robust anatomical features prior to human supervision.
  arXiv  Detail & Related papers  (2026-08-26T12:10:56Z)
• Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis [3.433052805056497]
  Lung-DDPM is a thoracic CT image synthesis approach that effectively generates high-fidelity 3D synthetic CT images.<n>Our results suggest that the proposed method outperforms other state-of-the-art generative models in image quality evaluation and downstream lung nodule segmentation tasks.<n>The experimental results highlight Lung-DDPM's potential for a broader range of medical imaging applications.
  arXiv  Detail & Related papers  (2025-02-21T04:38:27Z)
• Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging [65.83291923029985]
  Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment PCa lesions from a 200 CDI$
  arXiv  Detail & Related papers  (2025-01-15T22:23:41Z)
• Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection [8.500041312027596]
  We propose a novel variational autoencoder ($textCardioVAE_textX,G$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training. Our model also excels in producing fine interpretations of predictions directly associated with clinical features.
  arXiv  Detail & Related papers  (2024-03-20T15:06:49Z)
• Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI [6.21112347271845]
  Heart failure is a serious and life-threatening condition that can lead to elevated pressure in the left ventricle. PAWP is an important surrogate marker indicating high pressure in the left ventricle. A non-invasive method is useful in quickly identifying high-risk patients from a large population.
  arXiv  Detail & Related papers  (2023-03-14T00:05:08Z)
• COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging [101.27276001592101]
  We introduce COVID-Net US, a highly efficient, self-attention deep convolutional neural network design tailored for COVID-19 screening from lung POCUS images. Experimental results show that the proposed COVID-Net US can achieve an AUC of over 0.98 while achieving 353X lower architectural complexity, 62X lower computational complexity, and 14.3X faster inference times on a Raspberry Pi. To advocate affordable healthcare and artificial intelligence for resource-constrained environments, we have made COVID-Net US open source and publicly available as part of the COVID-Net open source initiative.
  arXiv  Detail & Related papers  (2021-08-05T16:47:33Z)
• Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images [58.85481248101611]
  We propose a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost.
  arXiv  Detail & Related papers  (2021-06-04T09:51:27Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings [2.408714894793063]
  Clinicians in the frontline need to assess quickly whether a patient with symptoms indeed has COVID-19 or not. The difficulty of this task is exacerbated in low resource settings that may not have access to biotechnology tests. We propose the extraction of deep features (DF) from chest X-ray images and their subsequent classification using machine learning methods.
  arXiv  Detail & Related papers  (2020-07-16T09:49:49Z)
• 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)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Deep Convolutional Neural Networks to Diagnose COVID-19 and other Pneumonia Diseases from Posteroanterior Chest X-Rays [0.0]
  Best performing network is VGG16 with a final $30$% drop-out trained over 3 classes (COVID-19, No Finding, Other Pneumonia) It has an internal cross-validated accuracy of $93.9(pm3.4)$%, a COVID-19 sensitivity of $87.7(-1.9,+2)$%, and a No Finding sensitivity of $96.8(pm0.8)$%.
  arXiv  Detail & Related papers  (2020-05-02T14:42:50Z)
• 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)
• Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation [0.0]
  We present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSCR of 97.5% on the JSRT dataset.
  arXiv  Detail & Related papers  (2020-03-23T14:45:48Z)

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.