AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor
Lesion Based on Deep Learning and FDG PET/CT
- URL: http://arxiv.org/abs/2209.01212v1
- Date: Wed, 31 Aug 2022 09:14:44 GMT
- Title: AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor
Lesion Based on Deep Learning and FDG PET/CT
- Authors: Shaonan Zhong, Junyang Mo, Zhantao Liu
- Abstract summary: We propose a novel training strategy to build deep learning models capable of systemic tumor segmentation.
Our method is validated on the training set of the AutoPET 2022 Challenge.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automatic segmentation of tumor lesions is a critical initial processing step
for quantitative PET/CT analysis. However, numerous tumor lesion with different
shapes, sizes, and uptake intensity may be distributed in different anatomical
contexts throughout the body, and there is also significant uptake in healthy
organs. Therefore, building a systemic PET/CT tumor lesion segmentation model
is a challenging task. In this paper, we propose a novel training strategy to
build deep learning models capable of systemic tumor segmentation. Our method
is validated on the training set of the AutoPET 2022 Challenge. We achieved
0.7574 Dice score, 0.0299 false positive volume and 0.2538 false negative
volume on preliminary test set.The code of our work is available on the
following link: https://github.com/ZZZsn/MICCAI2022-autopet.
Related papers
- MAPPING: Model Average with Post-processing for Stroke Lesion
Segmentation [57.336056469276585]
We present our stroke lesion segmentation model based on nnU-Net framework, and apply it to the Anatomical Tracings of Lesions After Stroke dataset.
Our method took the first place in the 2022 MICCAI ATLAS Challenge with an average Dice score of 0.6667, Lesion-wise F1 score of 0.5643, Simple Lesion Count score of 4.5367, and Volume Difference score of 8804.9102.
arXiv Detail & Related papers (2022-11-11T14:17:04Z) - 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) - PriorNet: lesion segmentation in PET-CT including prior tumor appearance
information [0.0]
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.
arXiv Detail & Related papers (2022-10-05T12:31:42Z) - 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) - AutoPET Challenge 2022: Step-by-Step Lesion Segmentation in Whole-body
FDG-PET/CT [0.0]
We propose a novel step-by-step 3D segmentation method to address this problem.
We achieved Dice score of 0.92, false positive volume of 0.89 and false negative volume of 0.53 on preliminary test set.
arXiv Detail & Related papers (2022-09-04T13:49:26Z) - EMT-NET: Efficient multitask network for computer-aided diagnosis of
breast cancer [58.720142291102135]
We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously.
We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions.
The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
arXiv Detail & Related papers (2022-01-13T05:24:40Z) - 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) - Medulloblastoma Tumor Classification using Deep Transfer Learning with
Multi-Scale EfficientNets [63.62764375279861]
We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions.
Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
arXiv Detail & Related papers (2021-09-10T13:07:11Z) - H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd
Place Solution to BraTS Challenge 2020 Segmentation Task [96.49879910148854]
Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions.
We trained and evaluated our model on the Multimodal Brain Tumor Challenge (BraTS) 2020 dataset.
Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.
arXiv Detail & Related papers (2020-12-30T20:44:55Z) - Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net
neural networks: a BraTS 2020 challenge solution [56.17099252139182]
We automate and standardize the task of brain tumor segmentation with U-net like neural networks.
Two independent ensembles of models were trained, and each produced a brain tumor segmentation map.
Our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff 95% of 20.4, 6.7 and 19.5mm on the final test dataset.
arXiv Detail & Related papers (2020-10-30T14:36:10Z)
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.