Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI

Vente, Coen de; Vos, Pieter C.; Hosseinzadeh, Matin; Pluim, Josien P. W.; Veta, Mitko

doi:10.1109/tbme.2020.2993528

Cited by 114 publications

(92 citation statements)

References 31 publications

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“…The authors implemented a multi-class DL algorithm with ordinal encoding incorporating both T2W and ADC images. Vente and colleagues described a 2D DL segmentation approach in which zonal masks were implemented along mpMRI [93]. Their work assigned different classes to lesions according to the probability of the output layer, with a higher ISUP group correlating to a higher probability.…”

Section: Multi-class Lesion Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Twilt¹,

Leeuwen²,

Huisman³

et al. 2021

Diagnostics

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Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

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Section: Multi-class Lesion Detectionmentioning

confidence: 99%

“…Their work assigned different classes to lesions according to the probability of the output layer, with a higher ISUP group correlating to a higher probability. A quadratic-weighted kappa score of 0.13 was achieved, indicating the still difficult task for lesion detection combined with grading [93].…”

Section: Multi-class Lesion Detectionmentioning

confidence: 99%

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Twilt¹,

Leeuwen²,

Huisman³

et al. 2021

Diagnostics

View full text Add to dashboard Cite

show abstract

“…In this study, we used SegNet and achieved comparable or superior results for PCa autosegmentation (DSC of 0.52) to those achieved in other studies using bpMRI or mpMRI (DSCs of 0.37-0.46) [34][35][36]. Our study achieved a result (AUC of 0.9) comparable to that achieved by other studies (AUCs of 0.84 [30] and 0.94 [14]) that used the AUC to determine the performance of PCa detection for the dataset and combination of images adopted in the present study.…”

Section: Discussionmentioning

confidence: 71%

Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks

Lai

Wang

et al. 2021

Sensors

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The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in detecting PCa. However, prostate MRI assessment relies on human experts and specialized training with considerable inter-reader variability. Deep learning may be a more robust approach for prostate MRI assessment. Here we present a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model. We used PROSTATEx dataset to train the model and combined different sequences into three channels of a single image. For each subject, all slices that contained the transition zone (TZ), peripheral zone (PZ), and PCa region were selected. The datasets were produced using different combinations of images, including T2-weighted (T2W) images, diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) images. Among these groups, the T2W + DWI + ADC images exhibited the best performance with a dice similarity coefficient of 90.45% for the TZ, 70.04% for the PZ, and 52.73% for the PCa region. Image sequence analysis with a DCNN model has the potential to assist PCa diagnosis.

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“…Network ensembles have been shown to create more robust results than single networks 58 , 59 . They leverage different minima that CNNs can obtain because networks are subject to randomness during training.…”

Section: Methodsmentioning

confidence: 99%

Domain adaptation for segmentation of critical structures for prostate cancer therapy

Meyer

Mehrtash

Rak

et al. 2021

Sci Rep

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Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model’s performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ($${p}<0.005$$ p < 0.005 for all structures using a Wilcoxon’s signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method’s generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted.

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Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI

Cited by 114 publications

References 31 publications

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks

Domain adaptation for segmentation of critical structures for prostate cancer therapy

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