Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation

• URL: http://arxiv.org/abs/2106.04961v1
• Date: Wed, 9 Jun 2021 10:15:20 GMT
• Title: Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation
• Authors: Kai-Chieh Liang, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
• Abstract summary: We propose a 'dual-stream' neural network (ST-DSNN) to segment sequential whole-body PET scans. Our ST-DSNN learns and accumulates image features from the PET images done over time. Our results show that our method outperforms the state-of-the-art PET image segmentation methods.
• Score: 10.344707825773252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential whole-body 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) scans are regarded as the imaging modality of choice for the assessment of treatment response in the lymphomas because they detect treatment response when there may not be changes on anatomical imaging. Any computerized analysis of lymphomas in whole-body PET requires automatic segmentation of the studies so that sites of disease can be quantitatively monitored over time. State-of-the-art PET image segmentation methods are based on convolutional neural networks (CNNs) given their ability to leverage annotated datasets to derive high-level features about the disease process. Such methods, however, focus on PET images from a single time-point and discard information from other scans or are targeted towards specific organs and cannot cater for the multiple structures in whole-body PET images. In this study, we propose a spatio-temporal 'dual-stream' neural network (ST-DSNN) to segment sequential whole-body PET scans. Our ST-DSNN learns and accumulates image features from the PET images done over time. The accumulated image features are used to enhance the organs / structures that are consistent over time to allow easier identification of sites of active lymphoma. Our results show that our method outperforms the state-of-the-art PET image segmentation methods.

Related papers

• st-DTPM: Spatial-Temporal Guided Diffusion Transformer Probabilistic Model for Delayed Scan PET Image Prediction [6.812140684449128]
  Several studies have indicated that dual-time PET imaging holds promise in distinguishing between malignant and benign tumor processes. We propose a novel spatial-temporal guided diffusion transformer probabilistic model (st-DTPM) to solve dual-time PET imaging prediction problem.
  arXiv  Detail & Related papers  (2024-10-30T06:37:55Z)
• Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness [43.45142393436787]
  We design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness. The pre-training phase is devised to explore both fine-grained discriminative features and effective semantic representation. The SPET prediction phase adopts a coarse prediction network utilizing pre-learned dose level prior to generate preliminary result.
  arXiv  Detail & Related papers  (2024-04-02T01:57:08Z)
• Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks [40.986069119392944]
  We propose MX-ARM, a multimodal MiXture-of-experts Alignment Reconstruction and Model. It is modality detachable and exchangeable, allocating different multi-layer perceptrons dynamically ("mixture of experts") through learnable weights to learn respective representations from different modalities.
  arXiv  Detail & Related papers  (2024-03-29T08:47:49Z)
• A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma [0.41579653852022364]
  We develop and validate a fast and efficient three-step cascaded deep learning model for automated detection and segmentation of DLBCL tumors from PET images. Our model is more effective than a single end-to-end network for segmentation of tumors in whole-body PET images.
  arXiv  Detail & Related papers  (2024-03-11T18:36:55Z)
• Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction [62.29541106695824]
  This paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM) By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process.
  arXiv  Detail & Related papers  (2023-08-20T04:10:36Z)
• LOTUS: Learning to Optimize Task-based US representations [39.81131738128329]
  Anatomical segmentation of organs in ultrasound images is essential to many clinical applications. Existing deep neural networks require a large amount of labeled data for training in order to achieve clinically acceptable performance. In this paper, we propose a novel approach for learning to optimize task-based ultra-sound image representations.
  arXiv  Detail & Related papers  (2023-07-29T16:29:39Z)
• CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images [10.994223928445589]
  High radioactivity caused by the injected tracer dose is a major concern in PET imaging. Reducing the dose leads to inadequate image quality for diagnostic practice. CNNs-based methods have been developed for high quality PET synthesis from its low-dose counterparts.
  arXiv  Detail & Related papers  (2023-04-03T05:39:02Z)
• Whole-Body Lesion Segmentation in 18F-FDG PET/CT [11.662584140924725]
  The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume.
  arXiv  Detail & Related papers  (2022-09-16T10:49:53Z)
• 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)
• Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning [14.60163613315816]
  Distant metastases (DM) are the leading cause of death in patients with soft-tissue sarcomas (STSs) It is difficult to determine from imaging studies which STS patients will develop metastases. We outline a new 3D CNN to help predict DM in patients from PET-CT data.
  arXiv  Detail & Related papers  (2021-04-23T05:12:02Z)
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images [46.844349956057776]
  coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. There is still lack of studies on effectively quantifying the lung infection caused by COVID-19. We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
  arXiv  Detail & Related papers  (2020-04-12T16:24:59Z)

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.