Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET Imaging

• URL: http://arxiv.org/abs/2409.13410v1
• Date: Fri, 20 Sep 2024 11:20:11 GMT
• Title: Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET Imaging
• Authors: Jintao Ren, Muheng Li, Stine Sofia Korreman,
• Abstract summary: This report presents a normalization block for automated tumor segmentation in CT/PET scans, developed for the autoPET III Challenge. The key innovation is the introduction of the SineNormal, which applies periodic sine transformations to PET data to enhance lesion detection.
• Score: 2.482413309706322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This report presents a normalization block for automated tumor segmentation in CT/PET scans, developed for the autoPET III Challenge. The key innovation is the introduction of the SineNormal, which applies periodic sine transformations to PET data to enhance lesion detection. By highlighting intensity variations and producing concentric ring patterns in PET highlighted regions, the model aims to improve segmentation accuracy, particularly for challenging multitracer PET datasets. The code for this project is available on GitHub (https://github.com/BBQtime/Sine-Wave-Normalization-for-Deep-Learning-Based-Tumor-Segmentation-in-CT -PET).

Related papers

• AutoPET III Challenge: Tumor Lesion Segmentation using ResEnc-Model Ensemble [1.3467243219009812]
  We trained a 3D Residual encoder U-Net within the no new U-Net framework to generalize the performance of automatic lesion segmentation. We leveraged test-time augmentations and other post-processing techniques to enhance tumor lesion segmentation. Our team currently hold the top position in the Auto-PET III challenge and outperformed the challenge baseline model in the preliminary test set with Dice score of 0.9627.
  arXiv  Detail & Related papers  (2024-09-19T20:18:39Z)
• Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT [4.376648893167674]
  The autoPET III Challenge focuses on advancing automated segmentation of tumor lesions in PET/CT images. We developed a classifier that identifies the tracer of the given PET/CT based on the Maximum Intensity Projection of the PET scan. Our final submission achieves cross-validation Dice scores of 76.90% and 61.33% for the publicly available FDG and PSMA datasets.
  arXiv  Detail & Related papers  (2024-09-18T17:16:57Z)
• Enhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing Techniques [2.4549652987344546]
  This research employs deep learning to enhance lesion segmentation in PET/CT imaging. Our methodical approach includes robust preprocessing and data augmentation techniques to ensure model robustness and generalizability. This study aims to contribute to the standardization of preprocessing and augmentation strategies in PET/CT imaging.
  arXiv  Detail & Related papers  (2024-09-15T16:27:34Z)
• From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging [0.9384264274298444]
  We present our solution for the autoPET III challenge, targeting multitracer, multicenter generalization using the nnU-Net framework with the ResEncL architecture. Key techniques include misalignment data augmentation and multi-modal pretraining across CT, MR, and PET datasets. Compared to the default nnU-Net, which achieved a Dice score of 57.61, our model significantly improved performance with a Dice score of 68.40, alongside a reduction in false positive (FPvol: 7.82) and false negative (FNvol: 10.35) volumes.
  arXiv  Detail & Related papers  (2024-09-14T16:39:17Z)
• AutoPET Challenge: Tumour Synthesis for Data Augmentation [26.236831356731017]
  We adapt the DiffTumor method, originally designed for CT images, to generate synthetic PET-CT images with lesions. Our approach trains the generative model on the AutoPET dataset and uses it to expand the training data. Our findings show that the model trained on the augmented dataset achieves a higher Dice score, demonstrating the potential of our data augmentation approach.
  arXiv  Detail & Related papers  (2024-09-12T14:23:19Z)
• 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)
• TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms [45.24575167909925]
  TriDoFormer is a transformer-based model that unites triple domains of sinogram, image, and frequency for direct reconstruction. It outperforms state-of-the-art methods qualitatively and quantitatively. GFP serves as a learnable frequency filter that adjusts the frequency components in the frequency domain, enforcing the network to restore high-frequency details.
  arXiv  Detail & Related papers  (2023-08-10T06:20:00Z)
• PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines [46.09869227806991]
  evaluating canine electrocardiograms (ECG) require skilled veterinarians. Current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. We implement a deep convolutional neural network (CNN) approach for classifying canine electrocardiogram sequences as either normal or abnormal.
  arXiv  Detail & Related papers  (2023-05-17T09:06:39Z)
• 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)
• Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation [51.916106055115755]
  We propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN) Our architecture is composed of three sequential modules that are estimated together during training. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners.
  arXiv  Detail & Related papers  (2021-07-06T14:50:03Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)

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.