Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?

• URL: http://arxiv.org/abs/2207.02854v1
• Date: Wed, 6 Jul 2022 07:55:46 GMT
• Title: Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?
• Authors: Audrey Duran (MYRIAD), Gaspard Dussert (MYRIAD), Carole Lartizien (MYRIAD)
• Abstract summary: We evaluate strategies to integrate information from perfusion imaging in deep neural architectures. Perfusion maps from dynamic contrast enhanced MR exams are shown to positively impact segmentation and grading performance of PCa lesions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence,which is however part of standard clinical protocols for this diagnostic task. In this paper, we question strategies to integrate information from perfusion imaging in deep neural architectures. To do so, we evaluate several ways to encode the perfusion information in a U-Net like architecture, also considering early versus mid fusion strategies. We compare performance of multiparametric MRI (mp-MRI) models with the baseline bp-MRI model based on a private dataset of 219 mp-MRI exams. Perfusion maps derived from dynamic contrast enhanced MR exams are shown to positively impact segmentation and grading performance of PCa lesions, especially the 3D MR volume corresponding to the maximum slope of the wash-in curve as well as Tmax perfusion maps. The latter mp-MRI models indeed outperform the bp-MRI one whatever the fusion strategy, with Cohen's kappa score of 0.318$\pm$0.019 for the bp-MRI model and 0.378 $\pm$ 0.033 for the model including the maximum slope with a mid fusion strategy, also achieving competitive Cohen's kappa score compared to state of the art.

Related papers

• Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging [65.83291923029985]
  Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment PCa lesions from a 200 CDI$
  arXiv  Detail & Related papers  (2025-01-15T22:23:41Z)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process. We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels [3.720246718519987]
  We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. We propose a self-supervised deep variational approach that breaks the assumption of independent pixels. Our approach can hence support the clinical adoption of parameter mapping methods such as dMRI and qMRI.
  arXiv  Detail & Related papers  (2024-11-16T11:11:36Z)
• 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)
• Evaluating the Impact of Sequence Combinations on Breast Tumor Segmentation in Multiparametric MRI [0.0]
  The effect of sequence combinations in mpMRI remains under-investigated. The nnU-Net model using DCE sequences achieved a Dice similarity coefficient (DSC) of 0.69 $pm$ 0.18 for functional tumor volume (FTV) segmentation.
  arXiv  Detail & Related papers  (2024-06-12T02:09:05Z)
• Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis [3.881664394416534]
  It is necessary to perform automatic segmentation of brain tumors on MRI images. This project intends to build an MRI algorithm based on U-Net.
  arXiv  Detail & Related papers  (2024-05-23T04:33:12Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis [0.0]
  MRI protocols may change over time or differ between centres resulting in low-quality sCT. domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation.
  arXiv  Detail & Related papers  (2023-03-17T18:45:05Z)
• 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)
• Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging [62.346649719614]
  preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment. We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
  arXiv  Detail & Related papers  (2022-09-13T07:10:14Z)
• 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)

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.