2,514 research outputs found
Processing of microCT implant-bone systems images using Fuzzy Mathematical Morphology
The relationship between a metallic implant and the existing bone in a surgical permanent prosthesis is of great importance since the fixation and osseointegration of the system leads to the failure or success of the surgery. Micro Computed Tomography is atechnique that helps to visualize the structure of the bone. In this study, the microCT is used to analyze implant-bone systems images. However, one of the problems presented in the reconstruction of these images is the effect of the iron based implants, with a halo or fluorescence scattering distorting the micro CT image and leading to bad 3D reconstructions.In this work we introduce an automatic method for eliminate the effect of AISI 316L iron materials in the implant-b one system based on the application of Compensatory Fuzzy Mathematical Morphology for future investigate about the structural and mechanical properties of bone and cancellous materials.Fil: Bouchet, Agustina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad Nacional de Mar del Plata; ArgentinaFil: Colabella, Lucas. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales. Universidad Nacional de Mar del Plata. Facultad de IngenierĂa. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales; ArgentinaFil: Omar, Sheila AyelĂ©n. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales. Universidad Nacional de Mar del Plata. Facultad de IngenierĂa. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales; ArgentinaFil: Ballarre, Josefina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales. Universidad Nacional de Mar del Plata. Facultad de IngenierĂa. Instituto de Investigaciones en Ciencia y TecnologĂa de Materiales; ArgentinaFil: Pastore, Juan Ignacio. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad Nacional de Mar del Plata; Argentin
Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences
In this article, we present a graph-based method using a cubic template for
volumetric segmentation of vertebrae in magnetic resonance imaging (MRI)
acquisitions. The user can define the degree of deviation from a regular cube
via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph
with two terminal nodes (s-t-network), where the nodes of the graph correspond
to a cubic-shaped subset of the image's voxels. The weightings of the graph's
terminal edges, which connect every node with a virtual source s or a virtual
sink t, represent the affinity of a voxel to the vertebra (source) and to the
background (sink). Furthermore, a set of infinite weighted and non-terminal
edges implements the smoothness term. After graph construction, a minimal
s-t-cut is calculated within polynomial computation time, which splits the
nodes into two disjoint units. Subsequently, the segmentation result is
determined out of the source-set. A quantitative evaluation of a C++
implementation of the algorithm resulted in an average Dice Similarity
Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338
Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net
Radiotherapy (RT) is a key component in the treatment of various cancers,
including Acute Lymphocytic Leukemia (ALL) and Acute Myelogenous Leukemia
(AML). Precise delineation of organs at risk (OARs) and target areas is
essential for effective treatment planning. Intensity Modulated Radiotherapy
(IMRT) techniques, such as Total Marrow Irradiation (TMI) and Total Marrow and
Lymph node Irradiation (TMLI), provide more precise radiation delivery compared
to Total Body Irradiation (TBI). However, these techniques require
time-consuming manual segmentation of structures in Computerized Tomography
(CT) scans by the Radiation Oncologist (RO). In this paper, we present a deep
learning-based auto-contouring method for segmenting Planning Target Volume
(PTV) for TMLI treatment using the U-Net architecture. We trained and compared
two segmentation models with two different loss functions on a dataset of 100
patients treated with TMLI at the Humanitas Research Hospital between 2011 and
2021. Despite challenges in lymph node areas, the best model achieved an
average Dice score of 0.816 for PTV segmentation. Our findings are a
preliminary but significant step towards developing a segmentation model that
has the potential to save radiation oncologists a considerable amount of time.
This could allow for the treatment of more patients, resulting in improved
clinical practice efficiency and more reproducible contours
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