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

Montagne, Sarah; Hamzaoui, Dimitri; Allera, Alexandre; Ezziane, Malek; Luzurier, Anna; Quint, Raphaëlle; Kalai, Mehdi; Ayache, Nicholas; Delingette, Hervé; Renard-Penna, Raphaële

doi:10.1186/s13244-021-01010-9

Cited by 36 publications

(20 citation statements)

References 23 publications

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“…The quality of produced segmentations is the highest in the mid-gland part, however our approach leads to an improvement in segmentation quality in all parts of the prostate. These results are in line with studies, e.g., 10 that show that the inter-observer variation is the largest in the base, smaller in the apex, and the smallest in the mid-gland.…”

Section: Resultssupporting

confidence: 93%

Data variation-aware medical image segmentation

Dushatskiy¹,

Lowe²,

Bosman³

et al. 2022

Preprint

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Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowledged to still be a problem. One way to automatically overcome this is to capture and exploit this variation explicitly. Here, we propose an approach that improves on our previous work in this area and explain how it potentially can improve clinical acceptance of (semi-)automatic segmentation methods.In contrast to a standard neural network that produces one segmentation, we propose to use a multi-path Unet network that produces multiple segmentation variants, presumably corresponding to the variations that reside in the dataset. Different paths of the network are trained on disjoint data subsets. Because a priori it may be unclear what variations exist in the data, the subsets should be automatically determined. This is achieved by searching for the best data partitioning with an evolutionary optimization algorithm. Because each network path can become more specialized when trained on a more homogeneous data subset, better segmentation quality can be achieved. In practical usage, various automatically produced segmentations can be presented to a medical expert, from which the preferred segmentation can be selected. In experiments with a real clinical dataset of CT scans with prostate segmentations, our approach provides an improvement of several percentage points in terms of Dice and surface Dice coefficients compared to when all network paths are trained on all training data. Noticeably, the largest improvement occurs in the upper part of the prostate that is known to be most prone to inter-observer segmentation variation.

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Section: Resultssupporting

confidence: 93%

Data variation-aware medical image segmentation

Dushatskiy¹,

Lowe²,

Bosman³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The apex and the base of the prostate are the most difficult regions to segment, which is in accordance with previous publications, involving human 14,15 or automatic raters 28,43 (see Figs. 9, 10, and 16).…”

Section: Discussionsupporting

confidence: 91%

“…Second, the prostate is subject to an important intersubject variability due to physiological differences in terms of shape, size, and tissue intensities. 14,15 Finally, sequences acquired from different MRI machines increase the variability in appearance of the prostate in T2w imaging.…”

Section: Introductionmentioning

confidence: 99%

Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use

Hamzaoui

Montagne²,

Renard-Penna³

et al. 2022

J. Med. Imag.

Self Cite

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Purpose: An accurate zonal segmentation of the prostate is required for prostate cancer (PCa) management with MRI.Approach: The aim of this work is to present UFNet, a deep learning-based method for automatic zonal segmentation of the prostate from T2-weighted (T2w) MRI. It takes into account the image anisotropy, includes both spatial and channelwise attention mechanisms and uses loss functions to enforce prostate partition. The method was applied on a private multicentric threedimensional T2w MRI dataset and on the public two-dimensional T2w MRI dataset ProstateX. To assess the model performance, the structures segmented by the algorithm on the private dataset were compared with those obtained by seven radiologists of various experience levels.Results: On the private dataset, we obtained a Dice score (DSC) of 93.90 AE 2.85 for the whole gland (WG), 91.00 AE 4.34 for the transition zone (TZ), and 79.08 AE 7.08 for the peripheral zone (PZ). Results were significantly better than other compared networks' (p-value < 0.05). On ProstateX, we obtained a DSC of 90.90 AE 2.94 for WG, 86.84 AE 4.33 for TZ, and 78.40 AE 7.31 for PZ. These results are similar to state-of-the art results and, on the private dataset, are coherent with those obtained by radiologists. Zonal locations and sectorial positions of lesions annotated by radiologists were also preserved.Conclusions: Deep learning-based methods can provide an accurate zonal segmentation of the prostate leading to a consistent zonal location and sectorial position of lesions, and therefore can be used as a helping tool for PCa diagnosis.

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“…Segmentation of ROI’s can be done manually, semi-automatically or automatically. Manual segmentations performed by clinical experts are typically used, however, performing this task manually has well documented limitations such as inter-observer variability and a significant clinical time burden, which limits its feasibility for radiomics analyses in larger datasets ( 51 , 52 ). Variability in manual segmentations has the effect of introducing bias into the evaluation of quantitative imaging biomarkers ( 53 ).…”

Section: Radiomics: a Methodological Overviewmentioning

confidence: 99%

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

et al. 2021

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Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.

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

Cited by 36 publications

References 23 publications

Data variation-aware medical image segmentation

Data variation-aware medical image segmentation

Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

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