Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach

Yin, Yin; Fotin, Sergei V.; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Türkbey, Barış; Choyke, Peter L.

doi:10.1117/12.911778

Cited by 16 publications

(8 citation statements)

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“…The automated contours delineation of CG‐based on the algorithm proposed in generated a mean DSC of 80% for CG from T 2 w MR images without the delineated contours of PZ, which is lower than the DSC result in our study. Litjens et al also mentioned two atlas‐based zonal segmentation methods without manual segmentation as initialization that generated the mean DSCs of 57.0±19.0% and 48.0±22% 48.0±22% with respect to PZ contours delineated, which are lower than the reported DSC of PZ contours delineated by the proposed approach.…”

Section: Resultscontrasting

confidence: 73%

Prostate contours delineation using interactive directional active contours model and parametric shape prior model

Derraz

Forzy

Delebarre

et al. 2015

Numer Methods Biomed Eng

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Prostate contours delineation on Magnetic Resonance (MR) images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Traditional active contours-based delineation algorithms are typically quite successful for piecewise constant images. Nevertheless, when MR images have diffuse edges or multiple similar objects (e.g. bladder close to prostate) within close proximity, such approaches have proven to be unsuccessful. In order to mitigate these problems, we proposed a new framework for bi-stage contours delineation algorithm based on directional active contours (DAC) incorporating prior knowledge of the prostate shape. We first explicitly addressed the prostate contour delineation problem based on fast globally DAC that incorporates both statistical and parametric shape prior model. In doing so, we were able to exploit the global aspects of contour delineation problem by incorporating a user feedback in contours delineation process where it is shown that only a small amount of user input can sometimes resolve ambiguous scenarios raised by DAC. In addition, once the prostate contours have been delineated, a cost functional is designed to incorporate both user feedback interaction and the parametric shape prior model. Using data from publicly available prostate MR datasets, which includes several challenging clinical datasets, we highlighted the effectiveness and the capability of the proposed algorithm. Besides, the algorithm has been compared with several state-of-the-art methods.

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Section: Resultscontrasting

confidence: 73%

Prostate contours delineation using interactive directional active contours model and parametric shape prior model

Derraz

Forzy

Delebarre

et al. 2015

Numer Methods Biomed Eng

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“…In this context, classic Computer Vision techniques have been mainly exploited on T2w MRI. For instance, early studies combined classi-fiers with statistical shape models [37] or deformable models [38]; Toth et al [28] employed active appearance models with multiple level sets for simultaneous zonal segmentation; Qiu et al [25] used a continuous max-flow model-the dual formulation of convex relaxed optimization with region consistency constraints [39]; in contrast, Makni et al [40] fused and processed 3D T2w, DWI, and contrast-enhanced T1w MR images by means of an evidential C-means algorithm [41]. As the first CNN-based method, Clark et al [42] detected DWI MR images with prostate relying on Visual Geometry Group (VGG) net [43], and then sequentially segmented WG and CG using U-Net [44].…”

Section: Related Workmentioning

confidence: 99%

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

et al. 2019

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Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since the frequency and severity of tumors differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net, i.e., one of the most effective CNNs in biomedical image segmentation. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i ) training on either each individual dataset or multiple prostate MRI datasets and (ii ) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three stateof-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/crossdataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training. Therefore, we should consider multi-dataset training and SE

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“…Yin et al [44] presented the layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) model. The LOGISMOS model contains both the shape and topology information during deformation [44].…”

Section: Shapementioning

confidence: 99%

“…The Markov model used for the description of 'pixel being prostate' allows a shape model to be built [42,43]. Yin et al [44] presented the layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) model. The LOGISMOS model contains both the shape and topology information during deformation [44].…”

Section: Shapementioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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Computer technology is widely used for multimodal image analysis of the prostate gland. Several techniques have been developed, most of which incorporate a priori knowledge extracted from organ features. Knowledge extraction and modeling are multi-step tasks. Here, we review these steps and classify the modeling according to the data analysis methods employed and the features used. We conclude with a survey of some clinical applications where these techniques are employed.

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Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach

Cited by 16 publications

References 0 publications

Prostate contours delineation using interactive directional active contours model and parametric shape prior model

Prostate contours delineation using interactive directional active contours model and parametric shape prior model

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

A survey of prostate modeling for image analysis

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