Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans

by   Jin Ye, et al.

Tumor lesion segmentation is one of the most important tasks in medical image analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission Tomography (FDG-PET) is a widely used technique to identify and quantify metabolically active tumors. However, since FDG-PET scans only provide metabolic information, healthy tissue or benign disease with irregular glucose consumption may be mistaken for cancer. To handle this challenge, PET is commonly combined with Computed Tomography (CT), with the CT used to obtain the anatomic structure of the patient. The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results. anatomic structure of the patient. The combination of PET and CT is promising to handle this challenge by utilizing metabolic and anatomic information. 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. The experimental results demonstrate that the vanilla U-Net with proper input shape can achieve satisfactory performance. Specifically, our method achieves first place in both preliminary and final leaderboards of the autoPET 2022 challenge. Our code is available at https://github.com/Yejin0111/autoPET2022_Blackbean.


page 1

page 2

page 3

page 4


AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net

Tumor segmentation in medical imaging is crucial and relies on precise d...

Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation

Numerous oncology indications have extensively quantified metabolically ...

A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images

Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined wit...

NaviAirway: a bronchiole-sensitive deep learning-based airway segmentation pipeline for planning of navigation bronchoscopy

Navigation bronchoscopy is a minimally invasive procedure in which docto...

Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding

Automatic parsing of human anatomies at instance-level from 3D computed ...

Boosting Liver and Lesion Segmentation from CT Scans By Mask Mining

In this paper we propose a novel procedure to improve liver and liver le...

Please sign up or login with your details

Forgot password? Click here to reset