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RESEARCH PRODUCT

A Survey of Prostate Segmentation Methodologies in Ultrasound, Magnetic Resonance and Computed Tomography Images

Fabrice MeriaudeauDésiré SidibéJoan C. VilanovaSoumya GhoseXavier LladóRobert MartíJhimli MitraArnau OliverJordi Freixenet

subject

Malemedicine.medical_specialty[INFO.INFO-IM] Computer Science [cs]/Medical ImagingHealth Informatics02 engineering and technology030218 nuclear medicine & medical imagingProstate -- Cancer-- DiagnosisPròstata -- Càncer -- Diagnòstic03 medical and health sciencesProstate cancerSpeckle pattern0302 clinical medicineProstateProstate -- Cancer -- Imaging0202 electrical engineering electronic engineering information engineering[INFO.INFO-IM]Computer Science [cs]/Medical ImagingMedicineHumansComputer visionSegmentationPròstata -- Càncer -- ImatgesUltrasonographyModalitiesModality (human–computer interaction)medicine.diagnostic_test[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryUltrasoundProstateMagnetic resonance imagingmedicine.diseaseMagnetic Resonance Imaging3. Good healthComputer Science Applicationsmedicine.anatomical_structureImatgeria mèdica020201 artificial intelligence & image processingArtificial intelligenceRadiologybusinessTomography X-Ray ComputedSoftwareAlgorithmsImaging systems in medicine

description

Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented This research has been funded by VALTEC 08-1-0039 of Generalitat de Catalunya, Spain and Conseil Regional de Bourgogne, France. The research is partially funded by Spanish Science and Innovation grant no. TIN2011-23704

https://hal.archives-ouvertes.fr/hal-00695557/file/CMPB_Soumya.pdf