Gland and Zonal Segmentation of Prostate on T2W MR Images

Chilali, Ouardia; Puech, Philippe; Lakroum, S.; Diaf, Moussa; Mordon, Serge; Betrouni, Nacim

doi:10.1007/s10278-016-9890-0

Cited by 33 publications

(27 citation statements)

References 14 publications

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“…The procedure utilizes a large array of existing robust utilities in MIM for image normalization and deformable fusion. The segmentation results (DSC of 0.83) are comparable to previously reported in atlas-based approaches: DSC of 0.85 in Klein 15 ; 0.82 in Chilali 21 ; 0.87 in Cheng 19 ; 0.87 in Xie 18 ; 0.83 in Tian 28 ; and 0.87-0.88 in Korsager 20 . Note, DSC=0.83 in this work is calculated over the entire prostate, while some of the referenced studies, e.g.…”

Section: Discussionsupporting

confidence: 83%

“…Several promising automatic, semi-automatic and interactive approaches were evaluated, 12 including atlas-based segmentation techniques [13][14][15] . Because of the excellent depiction of the prostate and surrounding anatomy, the high signal-to-noise ratio (SNR), and high spatial resolution, 12,16,17 T2-weighted MRI is the sequence of choice for building a prostate atlas 12,[18][19][20][21][22] . More recently, several studies provide also segmentation of the prostate zonal structures 21,[23][24][25] The presented approaches vary from model-based 26,27 , to atlasbased segmentation 15,[18][19][20][21]28 The goal of this work is to implement a robust procedure for prostate and prostate zones segmentation in a clinical imaging platform MIM (MIM Software Inc, Cleveland, OH, USA).…”

Section: Introductionmentioning

confidence: 99%

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Towards a universal MRI atlas of the prostate and prostate zones

et al. 2018

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Towards a universal MRI atlas of the prostate and prostate zones

et al. 2018

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“…Chilali et al 27 proposed an atlas-based and c-means clustering for prostate and zonal segmentation and achieved Dice values of 0.81, 0.70, and 0.62 for the prostate, the transition zone, and peripheral zone, respectively. Tian et.…”

Section: Introductionmentioning

confidence: 99%

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

Aldoj

Biavati

Michallek

et al. 2020

Sci Rep

103

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Magnetic resonance imaging (MRi) provides detailed anatomical images of the prostate and its zones. it has a crucial role for many diagnostic applications. Automatic segmentation such as that of the prostate and prostate zones from MR images facilitates many diagnostic and therapeutic applications. However, the lack of a clear prostate boundary, prostate tissue heterogeneity, and the wide interindividual variety of prostate shapes make this a very challenging task. to address this problem, we propose a new neural network to automatically segment the prostate and its zones. We term this algorithm Dense U-net as it is inspired by the two existing state-of-the-art tools-Densenet and U-net. We trained the algorithm on 141 patient datasets and tested it on 47 patient datasets using axial T2-weighted images in a four-fold cross-validation fashion. The networks were trained and tested on weakly and accurately annotated masks separately to test the hypothesis that the network can learn even when the labels are not accurate. the network successfully detects the prostate region and segments the gland and its zones. compared with U-net, the second version of our algorithm, Dense-2 U-net, achieved an average Dice score for the whole prostate of 92.1± 0.8% vs. 90.7 ± 2%, for the central zone of 89.5 ± 2 % vs. 89.1 ± 2.2 %, and for the peripheral zone of 78.1± 2.5% vs. 75 ± 3 %. Our initial results show Dense-2 U-net to be more accurate than state-of-the-art U-net for automatic segmentation of the prostate and prostate zones.

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“…Ou and Davatzikos (2012); Klein et al (2008) multi-atlas based approaches were applied to the whole prostate segmentation. Although automatic whole prostate segmentation has already been addressed in the literature, very few works have been proposed for zonal segmentation (Chilali et al, 2016;Makni et al, 2014;Qiu et al, 2014;Toth et al, 2013) and none of them use multi-atlas based methods.…”

Section: Introductionmentioning

confidence: 99%

GAS: A genetic atlas selection strategy in multi-atlas segmentation framework

Antonelli

Cardoso

Johnston

et al. 2019

Medical Image Analysis

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Multi-Atlas based Segmentation (MAS) algorithms have been successfully applied to many medical image segmentation tasks, but their success relies on a large number of atlases and good image registration performance. Choosing well-registered atlases for label fusion is vital for an accurate segmentation. This choice becomes even more crucial when the segmentation involves organs characterized by a high anatomical and pathological variability. In this paper, we propose a new genetic atlas selection strategy (GAS) that automatically chooses the best subset of atlases to be used for segmenting the target image, on the basis of both image similarity and segmentation overlap. More precisely, the key idea of GAS is that if two images are similar, the performances of an atlas for segmenting each image are similar. Since the ground truth of each atlas is known, GAS first selects a predefined number of similar images to the target, then, for each one of them, finds a near-optimal subset of atlases by means of a genetic algorithm. All these near-optimal subsets are then combined and used to segment the target image. GAS was tested on single-label and multi-label segmentation problems. In the first case, we considered the segmentation of both the whole prostate and of the left ventricle of the heart from magnetic

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Gland and Zonal Segmentation of Prostate on T2W MR Images

Cited by 33 publications

References 14 publications

Towards a universal MRI atlas of the prostate and prostate zones

Towards a universal MRI atlas of the prostate and prostate zones

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

GAS: A genetic atlas selection strategy in multi-atlas segmentation framework

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