Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net

Aldoj, Nader; Biavati, Federico; Michallek, Florian; Stober, Sebastian; Dewey, Marc

doi:10.1038/s41598-020-71080-0

Cited by 104 publications

(87 citation statements)

References 48 publications

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“…In such a context, automated prostate segmentation approaches 68 , 69 can accelerate the outlining time in manual segmentation procedures, as well as reduce the operator dependence for repeatable radiomic feature extraction. In addition, prostate zonal segmentation 70 , 71 might also be considered for extracting zone-specific radiomics biomarkers. Furthermore, as the intrinsic inconsistency of weighted images often limits the generalisability of radiomics in a multi-center context, texture analysis of both conventional and novel quantitative mapping techniques that have already been studied in PCa 72 is an area of interest.…”

Section: Discussionmentioning

confidence: 99%

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Sushentsev

Rundo

Blyuss

et al. 2021

Sci Rep

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Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481–0.743) to 0.75 (95% CI 0.64–0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.

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Section: Discussionmentioning

confidence: 99%

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Sushentsev

Rundo

Blyuss

et al. 2021

Sci Rep

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“…Thus, the focus for automatic prostate segmentation went from whole gland segmentation to zonal segmentation of the gland [11,12], which is now necessary for the development of AI algorithms for prostate cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology

Montagne

Hamzaoui²,

Allera

et al. 2021

Insights Imaging

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Background Accurate prostate zonal segmentation on magnetic resonance images (MRI) is a critical prerequisite for automated prostate cancer detection. We aimed to assess the variability of manual prostate zonal segmentation by radiologists on T2-weighted (T2W) images, and to study factors that may influence it. Methods Seven radiologists of varying levels of experience segmented the whole prostate gland (WG) and the transition zone (TZ) on 40 axial T2W prostate MRI images (3D T2W images for all patients, and both 3D and 2D images for a subgroup of 12 patients). Segmentation variabilities were evaluated based on: anatomical and morphological variation of the prostate (volume, retro-urethral lobe, intensity contrast between zones, presence of a PI-RADS ≥ 3 lesion), variation in image acquisition (3D vs 2D T2W images), and reader’s experience. Several metrics including Dice Score (DSC) and Hausdorff Distance were used to evaluate differences, with both a pairwise and a consensus (STAPLE reference) comparison. Results DSC was 0.92 (± 0.02) and 0.94 (± 0.03) for WG, 0.88 (± 0.05) and 0.91 (± 0.05) for TZ respectively with pairwise comparison and consensus reference. Variability was significantly (p < 0.05) lower for the mid-gland (DSC 0.95 (± 0.02)), higher for the apex (0.90 (± 0.06)) and the base (0.87 (± 0.06)), and higher for smaller prostates (p < 0.001) and when contrast between zones was low (p < 0.05). Impact of the other studied factors was non-significant. Conclusions Variability is higher in the extreme parts of the gland, is influenced by changes in prostate morphology (volume, zone intensity ratio), and is relatively unaffected by the radiologist’s level of expertise.

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“…They achieved a DSC of 86% in the TZ and 74% in the PZ. Aldoj et al [28] also used T2W images of the PROSTATEx dataset. They used a DenseNet-like U-Net for training and achieved a DSC of 89.5% ± 2% in the central gland region and 78.1% ± 2.5% in the PZ.…”

Section: Discussionmentioning

confidence: 99%

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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Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net

Cited by 104 publications

References 48 publications

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology

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

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