Automated classification of multi-parametric body MRI series

• URL: http://arxiv.org/abs/2405.08247v1
• Date: Tue, 14 May 2024 00:39:21 GMT
• Title: Automated classification of multi-parametric body MRI series
• Authors: Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm, Ronald M. Summers,
• Abstract summary: We propose an automated framework to classify the type of 8 different series in mpMRI studies. We used 1,363 studies acquired by three Siemens scanners to train a DenseNet-121 model with 5-fold cross-validation. Our method achieved an average precision of 96.6%, sensitivity of 96.6%, specificity of 99.6%, and F1 score of 96.6% for the MRI series classification task.
• Score: 7.039977392090069
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-parametric MRI (mpMRI) studies are widely available in clinical practice for the diagnosis of various diseases. As the volume of mpMRI exams increases yearly, there are concomitant inaccuracies that exist within the DICOM header fields of these exams. This precludes the use of the header information for the arrangement of the different series as part of the radiologist's hanging protocol, and clinician oversight is needed for correction. In this pilot work, we propose an automated framework to classify the type of 8 different series in mpMRI studies. We used 1,363 studies acquired by three Siemens scanners to train a DenseNet-121 model with 5-fold cross-validation. Then, we evaluated the performance of the DenseNet-121 ensemble on a held-out test set of 313 mpMRI studies. Our method achieved an average precision of 96.6%, sensitivity of 96.6%, specificity of 99.6%, and F1 score of 96.6% for the MRI series classification task. To the best of our knowledge, we are the first to develop a method to classify the series type in mpMRI studies acquired at the level of the chest, abdomen, and pelvis. Our method has the capability for robust automation of hanging protocols in modern radiology practice.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks [7.734037486455235]
  We propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis. To the best of our knowledge, we are the first to develop an automated method for the 3D classification of MRI sequences in the chest, abdomen, and pelvis.
  arXiv  Detail & Related papers  (2024-02-12T22:34:57Z)
• Radiomics Boosts Deep Learning Model for IPMN Classification [3.4659499358648675]
  Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from MRI scans.
  arXiv  Detail & Related papers  (2023-09-11T22:41:52Z)
• Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier [1.487444917213389]
  This paper proposed a lightweight 3D convolutional neural network (CNN) based framework for schizophrenia diagnosis using MRI images. The model achieves the highest accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall 94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-11-05T10:27:37Z)
• Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO) [0.12175619840081271]
  We propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-Oncology MRI data. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, and iv) delineates tumor tissue subtypes. It is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists.
  arXiv  Detail & Related papers  (2022-10-06T18:23:42Z)
• 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)
• CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images [2.0791118244420757]
  A two-head convolutional neural network ('CardiSort') was trained to classify 35 sequences by imaging sequence and plane. High sequence and plane accuracies were observed for single vendor training (SVT) and multi-vendor training (MVT) There was high accuracy for common sequences and conventional cardiac planes.
  arXiv  Detail & Related papers  (2021-09-17T11:42:39Z)
• A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI [70.04389979779195]
  We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies. We demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
  arXiv  Detail & Related papers  (2020-07-06T09:37:12Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes [64.21642241351857]
  We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. We developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports. We also developed a model for multi-organ, multi-disease classification of chest CT volumes.
  arXiv  Detail & Related papers  (2020-02-12T00:59:23Z)

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.