PriorNet: lesion segmentation in PET-CT including prior tumor appearance information

• URL: http://arxiv.org/abs/2210.02203v1
• Date: Wed, 5 Oct 2022 12:31:42 GMT
• Title: PriorNet: lesion segmentation in PET-CT including prior tumor appearance information
• Authors: Simone Bendazzoli and Mehdi Astaraki
• Abstract summary: We propose a two-step approach to improve the segmentation performances of tumoral lesions in PET-CT images. The first step generates a prior tumor appearance map from the PET-CT volumes, regarded as prior tumor information. The second step, consisting of a standard U-Net, receives the prior tumor appearance map and PET-CT images to generate the lesion mask.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tumor segmentation in PET-CT images is challenging due to the dual nature of the acquired information: low metabolic information in CT and low spatial resolution in PET. U-Net architecture is the most common and widely recognized approach when developing a fully automatic image segmentation method in the medical field. We proposed a two-step approach, aiming to refine and improve the segmentation performances of tumoral lesions in PET-CT. The first step generates a prior tumor appearance map from the PET-CT volumes, regarded as prior tumor information. The second step, consisting of a standard U-Net, receives the prior tumor appearance map and PET-CT images to generate the lesion mask. We evaluated the method on the 1014 cases available for the AutoPET 2022 challenge, and the results showed an average Dice score of 0.701 on the positive cases.

Related papers

• CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach [4.4202829112545015]
  Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importance, the use of PET/CT systems is limited by the necessity for radioactive materials, the scarcity of PET scanners, and the high cost associated with PET imaging. In response to these challenges, our study addresses the issue of generating PET images from CT images, aiming to reduce both the medical examination cost and the associated health risks for patients.
  arXiv  Detail & Related papers  (2024-10-29T10:48:52Z)
• 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)
• AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net [30.142259166452693]
  FDG-PET scans may misinterpret irregular glucose consumption in healthy tissues as cancer. The AutoPET challenge addresses this by providing a dataset of 1014 FDG-PET/CT studies.
  arXiv  Detail & Related papers  (2023-09-21T14:34:17Z)
• 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)
• Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks [2.735686397209314]
  The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images. A cascaded approach was developed where a stacked ensemble of 3D UNET CNN processed the PET/CT images at a fixed 6mm resolution.
  arXiv  Detail & Related papers  (2022-10-14T19:25:56Z)
• Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans [16.93163630413171]
  Since FDG-PET scans only provide metabolic information, healthy tissue or benign disease with irregular glucose consumption may be mistaken for cancer. In this paper, we explore the potential of U-Net for lesion segmentation in whole-body FDG-PET/CT scans from three aspects, including network architecture, data preprocessing, and data augmentation. Our method achieves first place in both preliminary and final leaderboards of the autoPET 2022 challenge.
  arXiv  Detail & Related papers  (2022-10-14T03:37:18Z)
• Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images [12.885308856495353]
  In PET/CT image assessment, automatic tumor segmentation is an important step. Existing methods tend to over-segment the tumor regions and include regions such as the normal high organs, inflammation, and other infections. We introduce a false positive reduction network to overcome this limitation.
  arXiv  Detail & Related papers  (2022-09-16T04:01:14Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)
• 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)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

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.