Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy

• URL: http://arxiv.org/abs/2208.11468v1
• Date: Wed, 24 Aug 2022 12:18:25 GMT
• Title: Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy
• Authors: Ron Keuth, Mattias Heinrich, Martin Eichenlaub and Marian Himstedt
• Abstract summary: This paper addresses the automatic segmentation of bronchial orifices in bronchoscopy videos. Deep learning-based approaches to this task are currently hampered due to the lack of readily-available ground truth segmentation data. We present a data-driven pipeline consisting of a k-means followed by a compact marker-based watershed algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Video bronchoscopy is routinely conducted for biopsies of lung tissue suspected for cancer, monitoring of COPD patients and clarification of acute respiratory problems at intensive care units. The navigation within complex bronchial trees is particularly challenging and physically demanding, requiring long-term experiences of physicians. This paper addresses the automatic segmentation of bronchial orifices in bronchoscopy videos. Deep learning-based approaches to this task are currently hampered due to the lack of readily-available ground truth segmentation data. Thus, we present a data-driven pipeline consisting of a k-means followed by a compact marker-based watershed algorithm which enables to generate airway instance segmentation maps from given depth images. In this way, these traditional algorithms serve as weak supervision for training a shallow CNN directly on RGB images solely based on a phantom dataset. We evaluate generalization capabilities of this model on two in-vivo datasets covering 250 frames on 21 different bronchoscopies. We demonstrate that its performance is comparable to those models being directly trained on in-vivo data, reaching an average error of 11 vs 5 pixels for the detected centers of the airway segmentation by an image resolution of 128x128. Our quantitative and qualitative results indicate that in the context of video bronchoscopy, phantom data and weak supervision using non-learning-based approaches enable to gain a semantic understanding of airway structures.

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)
• BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images [21.19843479423806]
  Chest X-rays (CXRs) are commonly utilized as a low-dose modality for lung screening. Approximately 75% of the lung area overlaps with bone, which in turn hampers the detection and diagnosis of diseases. Bone suppression techniques have been introduced, but the current dual-energy subtraction imaging technique in the clinic requires costly equipment and subjects being exposed to high radiation. This paper proposes a new bone suppression framework, termed BS-Diff, that comprises a conditional diffusion model equipped with a U-Net architecture and a simple enhancement module to incorporate an autoencoder.
  arXiv  Detail & Related papers  (2023-11-26T15:13:13Z)
• Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge [0.0]
  This paper addresses navigation guidance solely incorporating bronchosopy video data. We take maximally advantage of anatomical constraints of airway trees being sequentially traversed. We are able to improve the accuracy up to to 0.98 compared to 0.81 for a classification based on individual frames.
  arXiv  Detail & Related papers  (2023-07-17T08:26:36Z)
• 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)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• 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)
• Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection [11.228544549618068]
  Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. We show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization. An evolutionary pruning mechanism is used to train a nodule detection deep learning model on the most informative images from a publicly available lung nodule X-ray dataset.
  arXiv  Detail & Related papers  (2022-01-24T10:08:07Z)
• 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)
• 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)
• Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images [5.826056983051642]
  A chest radiograph, commonly called chest x-ray (CxR), plays a vital role in the diagnosis of various lung diseases. Precise lung segmentation is extremely challenging because of variance in the shape of the lung caused by health issues, age, and gender. The proposed work investigates the use of an efficient deep convolutional neural network for accurate segmentation of lungs from CxR.
  arXiv  Detail & Related papers  (2020-11-19T07:21:02Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)

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.