Prostate cancer segmentation with multispectral MRI using cost-sensitive Conditional Random Fields

Artan, Yusuf; Langer, Deanna L.; Haider, Masoom A.; Kwast, Theodorus H. van der; Evans, A.; Wernick, Miles N.; Yetik, İmam Şamil

doi:10.1109/isbi.2009.5193038

Cited by 24 publications

(21 citation statements)

References 5 publications

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“…Artan et al [114,115] as well as Ozer et al [98,99] standardized T 2 -W, DCE and DW MRI images by computing the standard score (also called z-score) of the pixels of the PZ. In a similar way, Liu et al [116] normalized T 2 -W-MRI by making use of the median and interquartile range for all the pixels.…”

Section: Image Regularization Frameworkmentioning

confidence: 99%

“…− Manual segmentation: To highlight the importance of prostate segmentation task in CAD systems, it is interesting to note the large number of studies which manually segment the prostate organs [114,115,135,121,122,98,99,136,137,138]. In all the cases, the boundaries of the prostate gland are manually defined in order to limit the further processing to only this area.…”

Section: Segmentationmentioning

confidence: 99%

“…Two approaches are considered to define a CADe (see Table 6): (i) voxel-based delineation and (ii) lesion segmentation. The first strategy, which concerns the majority of the studies reviewed (see Table 6), is in fact linked to the nature of the classification framework [114,115,163,128,166,104,135,167,98,99,126,168,169,170,151,171,172,132,173,174,145,110,118,119]. All voxels are considered as a possible lesion and the output of the framework will be pixels classified as lesion and non lesion.…”

Section: Cade: Rois Detection/selectionmentioning

confidence: 99%

See 2 more Smart Citations

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Lemaître¹,

Martí²,

Freixenet³

et al. 2015

Computers in Biology and Medicine

259

180

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Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.

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Section: Image Regularization Frameworkmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

Section: Cade: Rois Detection/selectionmentioning

confidence: 99%

See 1 more Smart Citation

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Lemaître¹,

Martí²,

Freixenet³

et al. 2015

Computers in Biology and Medicine

259

180

View full text Add to dashboard Cite

show abstract

“…Guo et al presented a deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching [27]. Moreover, the use of Support Vector Machines (SVM) with Conditional Random Fields has been reported to have an increased accuracy in delineating the region of interest [28]. The use of Markov Random Fields, coupled with multispectral MRI, has also been proposed for prostate cancer segmentation [29].…”

Section: Haptic Interfacementioning

confidence: 99%

Tele-Operated MRI-Guided Needle Insertion for Prostate Interventions

Moreira

Kuil

Dias

et al. 2019

J. Med. Robot. Res.

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Prostate cancer is one of the leading causes of death in men. Prostate interventions using magnetic resonance imaging (MRI) benefits from high tissue contrast if compared to other imaging modalities. The Minimally Invasive Robotics In An MRI environment (MIRIAM) robot is an MRI-compatible system able to steer different types of needles towards a point of interest using MRI guidance. However, clinicians can be reluctant to give the robot total control of the intervention. This work integrates a haptic device in the MIRIAM system to allow input from the clinician during the insertion. A shared control architecture is achieved by letting the clinician control the insertion depth via the haptic device, while the robotic system controls the needle orientation. The clinician receives haptic feedback based on the insertion depth and tissue characteristics. Four control laws relating the motion of the master robot (haptic device) to the motion of the slave robot (MIRIAM robot) are presented and evaluated. Quantitative and qualitative results from 20 human subjects demonstrate that the squared-velocity control law is the most suitable option for our application. Additionally, a pre-operative target localization algorithm is presented in order to provide the robot with the target location. The target localization and reconstruction algorithm are validated in phantom and patient images with an average dice similarity coefficient (DSC) of 0.78. The complete system is validated through experiments by inserting a needle towards a target within the MRI scanner. Four human subjects perform the experiment achieving an average targeting error of 3.4[Formula: see text]mm.

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“…3 However, little attention has been dedicated to the problem of normalization of T2W-MRI prostate images. 3 Artan et al 4,5 and Ozer et al 6,7 proposed to normalize the T2W-MRI images by computing the standard score (i.e., z-score) of the Peripheral Zone (PZ) pixels such as:…”

Section: Introductionmentioning

confidence: 99%

Normalization of T2W-MRI prostate images using Rician a priori

Lemaître

Rastgoo

Massich

et al. 2016

Medical Imaging 2016: Computer-Aided Diagnosis

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Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and overcome the inter-patients intensity variations. However, little attention has been dedicated to the normalization of T2W-Magnetic Resonance Imaging (MRI) prostate images. In this paper, we propose two methods to normalize T2W-MRI prostate images: (i) based on a Rician a priori and (ii) based on a Square-Root Slope Function (SRSF) representation which does not make any assumption regarding the Probability Density Function (PDF) of the data. A comparison with the state-of-the-art methods is also provided. The normalization of the data is assessed by comparing the alignment of the patient PDFs in both qualitative and quantitative manners. In both evaluation, the normalization using Rician a priori outperforms the other state-of-the-art methods.

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Prostate cancer segmentation with multispectral MRI using cost-sensitive Conditional Random Fields

Cited by 24 publications

References 5 publications

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Tele-Operated MRI-Guided Needle Insertion for Prostate Interventions

Normalization of T2W-MRI prostate images using Rician a priori

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