Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans.
Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Nonetheless, automatic segmentation is challenging due to the lack of clear prostate boundaries owing to the similar appearance...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 9; pp. 1947 - 1965 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145048074&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145048074 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2020 vid: 58 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145048074 144436460 145048074 NLM32566988 10.1007/s11517-020-02199-5 NLM32566988 145048074 ppf: 1947 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans. aug: au: da Silva, Giovanni L. F. Diniz, Petterson S. Ferreira, Jonnison L. França, João V. F. Silva, Aristófanes C. de Paiva, Anselmo C. de Cavalcanti, Elton A. A. affil: Applied Computing Group - NCA, Federal University of Maranhão - UFMA, Av. dos Portugueses, SN, Bacanga, 65085-580, São Luís, MA, Brazil sug: subj: Imaging, Three-Dimensional Statistics and Numerical Data Prostatic Neoplasms Image Interpretation, Computer Assisted Statistics and Numerical Data Magnetic Resonance Imaging Statistics and Numerical Data Male Algorithms Models, Statistical Resource Databases Male ab: Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Nonetheless, automatic segmentation is challenging due to the lack of clear prostate boundaries owing to the similar appearance of prostate and surrounding tissues and the wide variation in size and shape among different patients ascribed to pathological changes or different resolutions of images. In this regard, the state-of-the-art includes methods based on a probabilistic atlas, active contour models, and deep learning techniques. However, these techniques have limitations that need to be addressed, such as MRI scans with the same spatial resolution, initialization of the prostate region with well-defined contours and a set of hyperparameters of deep learning techniques determined manually, respectively. Therefore, this paper proposes an automatic and novel coarse-to-fine segmentation method for prostate 3D MRI scans. The coarse segmentation step combines local texture and spatial information using the Intrinsic Manifold Simple Linear Iterative Clustering algorithm and probabilistic atlas in a deep convolutional neural networks model jointly with the particle swarm optimization algorithm to classify prostate and non-prostate tissues. Then, the fine segmentation uses the 3D Chan-Vese active contour model to obtain the final prostate surface. The proposed method has been evaluated on the Prostate 3T and PROMISE12 databases presenting a dice similarity coefficient of 84.86%, relative volume difference of 14.53%, sensitivity of 90.73%, specificity of 99.46%, and accuracy of 99.11%. Experimental results demonstrate the high performance potential of the proposed method compared to those previously published. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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