INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET

N. Tillmanns,J. Lost,S. Merkaj,M. von Reppert, S. Chadha,M. Lin,K. Bousabarah,A. Huttner,S. Aneja,A. Avesta,A. Omuro,M. Aboian

NEURO-ONCOLOGY（2023）

Yale Sch Med | Visage Imaging Inc | Visage Imaging GmbH

Cited 0|Views26

Abstract

Abstract BACKGROUND Translation of AI algorithms into clinical practice is significantly limited by lack of large individual hospital-based datasets with expert annotations. Current methods for generation of annotated imaging data are significantly limited due to inefficient imaging data transfer, complicated annotation software, and time required for experts to generate ground truth information. We incorporated AI tools for auto-segmentation of gliomas into PACS that is used at our institution for reading clinical studies and developed a workflow for annotation of images and development of volumetric segmentations in neuroradiology clinical workflow. MATERIAL AND METHODS 1990 patients from Yale Radiation Oncology Registry (2012-2019) were screened. Segmentations were performed using a UNETR algorithm trained on BRaTS 2021 and an internal dataset of manually segmented tumors. AI generated segmentation can be revised and confirmed in PACS. Segmentations were validated by a board-certified neuro-radiologist, after which natively embedded PyRadiomics in PACS was used for direct feature extraction. RESULTS In 7 Months (05/2021 - 08/2021, 03/2022 - 05/2022) segmentations and annotations were performed in 1033 patients (429 female, 604 male, mean age 53 yrs). Dataset includes 595 Grade 4 Gliomas (96 Grade 3, 105 Grade 2, 45 Grade 1, 192 unknown). Molecular subtypes include IDH (129 mutated, 651 wildtype, 253 unknown), 1p/19q (94 deleted or co-deleted, 135 intact, 804 unknown), MGMT promotor (216 methylated, 110 partially methylated, 321 unmethylated, 383 unknown), EGFR (125 amplified, 248 not amplified, 660 unknown), ATRX (41 mutated, 238 retained, 754 unknown), Ki-67 (726 known, 307 unknown) and p53 (639 known, 394 unknown). Classification of gliomas between grade 3/4 and grade 1/2, yielded AUC of 0.85. CONCLUSION We developed a method for incorporation of volumetric segmentation, feature extraction, and classification that is easily incorporated into neuroradiology workflow. These tools allowed us to annotate over 100 gliomas per month, thus establishing a proof of concept for rapid development of annotated imaging database for AI applications.

Translated text

Key words

Medical Image Analysis,Image Segmentation

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

【要点】：本文提出了一种将基于AI的自动分割和分类技术集成到神经放射学工作流程中，并构建了Yale胶质瘤数据集，提高了注释成像数据库的效率和质量。

【方法】：通过在PACS系统中集成UNetR算法，实现了胶质瘤的自动分割，并结合内部手动分割数据集进行训练，随后在PACS中完成图像注释和体积分割。

【实验】：在7个月的时间内，对1990名患者的数据进行了分割和注释，最终构建了一个包含595例Grade 4胶质瘤的数据集，数据集通过PACS系统中的PyRadiomics进行特征提取，并在Grade 3/4与Grade 1/2之间的分类中达到了0.85的AUC。