Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation

• URL: http://arxiv.org/abs/2307.12634v1
• Date: Mon, 24 Jul 2023 09:16:05 GMT
• Title: Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation
• Authors: Qi Su, Na Wang, Jiawen Xie, Yinan Chen, Xiaofan Zhang
• Abstract summary: We propose a new automatic lung lobe segmentation framework, which pays attention to the area around the pulmonary fissure during the training process. We also introduce an end-to-end pulmonary fissure generation method in the auxiliary pulmonary fissure segmentation task. We achieve 97.83% and 94.75% dice scores on our private dataset STLB and public LUNA16 dataset respectively.
• Score: 6.0255364788259165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automatic lung lobe segmentation algorithm is of great significance for the diagnosis and treatment of lung diseases, however, which has great challenges due to the incompleteness of pulmonary fissures in lung CT images and the large variability of pathological features. Therefore, we propose a new automatic lung lobe segmentation framework, in which we urge the model to pay attention to the area around the pulmonary fissure during the training process, which is realized by a task-specific loss function. In addition, we introduce an end-to-end pulmonary fissure generation method in the auxiliary pulmonary fissure segmentation task, without any additional network branch. Finally, we propose a registration-based loss function to alleviate the convergence difficulty of the Dice loss supervised pulmonary fissure segmentation task. We achieve 97.83% and 94.75% dice scores on our private dataset STLB and public LUNA16 dataset respectively.

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)
• Automatic segmentation of lung findings in CT and application to Long COVID [38.69538648742266]
  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.
  arXiv  Detail & Related papers  (2023-10-13T23:42:43Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• What Makes for Automatic Reconstruction of Pulmonary Segments [50.216231776343115]
  3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer. However, automatic reconstruction of pulmonary segments remains unexplored in the era of deep learning. We propose ImPulSe, a deep implicit surface model designed for pulmonary segment reconstruction.
  arXiv  Detail & Related papers  (2022-07-07T04:24:17Z)
• 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)
• Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs [0.19573380763700707]
  Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes. This impact motivated developing an improved machine learning method to segment lung lobes. The approach can be readily adopted in the clinical setting as a robust tool for radiologists.
  arXiv  Detail & Related papers  (2021-05-11T17:10:25Z)
• Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)
• 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)
• Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function [0.0]
  We trained a 3D u-net for pulmonary lobe segmentation on 49 datasets. We introduced a weighted Dice loss function to emphasize the lobar boundaries.
  arXiv  Detail & Related papers  (2020-05-29T21:18:34Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)

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.