Improved threshold selection for the determination of volume of distribution of nanoparticles administered by convection-enhanced delivery

Lei, David; Song, Eric; Gaudin, Alice; Saltzman, W. Mark

doi:10.1016/j.compmedimag.2017.08.001

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Slides were imaged using a Zeiss Lumar V12 stereoscope (Carl Zeiss AG, Germany). Volume of distribution was quantified using a MATLAB code setting a threshold with Otsu’s method [33].…”

Section: Methodsmentioning

confidence: 99%

Biodegradable PEG-poly(ω-pentadecalactone-co-p-dioxanone) nanoparticles for enhanced and sustained drug delivery to treat brain tumors

et al. 2018

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Intracranial delivery of therapeutic agents is limited by penetration beyond the blood-brain barrier (BBB) and rapid metabolism of the drugs that are delivered. Convection-enhanced delivery (CED) of drug-loaded nanoparticles (NPs) provides for local administration, control of distribution, and sustained drug release. While some investigators have shown that repeated CED procedures are possible, longer periods of sustained release could eliminate the need for repeated infusions, which would enhance safety and translatability of the approach. Here, we demonstrate that nanoparticles formed from poly(ethylene glycol)-poly(ω-pentadecalactone-co-p-dioxanone) block copolymers [PEG-poly(PDL-co-DO)] are highly efficient nanocarriers that provide long-term release: small nanoparticles (less than 100 nm in diameter) continuously released a radiosensitizer (VE822) over a period of several weeks in vitro, provided widespread intracranial drug distribution during CED, and yielded significant drug retention within the brain for over 1 week. One advantage of PEG-poly(PDL-co-DO) nanoparticles is that hydrophobicity can be tuned by adjusting the ratio of hydrophobic PDL to hydrophilic DO monomers, thus making it possible to achieve a wide range of drug release rates and drug distribution profiles. When administered by CED to rats with intracranial RG2 tumors, and combined with a 5-day course of fractionated radiation therapy, VE822-loaded PEG-poly(PDL-co-DO) NPs significantly prolonged survival when compared to free VE822. Thus, PEG-poly(PDL-co-DO) NPs represent a new type of versatile nanocarrier system with potential for sustained intracranial delivery of therapeutic agents to treat brain tumors.

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“…Slides were imaged using a Zeiss Lumar V12 stereoscope (Carl Zeiss AG, Germany). Volume of distribution was quantified using a MATLAB code setting a threshold with Otsu’s method [33].…”

Section: Methodsmentioning

confidence: 99%

Biodegradable PEG-poly(ω-pentadecalactone-co-p-dioxanone) nanoparticles for enhanced and sustained drug delivery to treat brain tumors

et al. 2018

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“…Otsu and Canny edge detection technique is used to determine the grain boundaries of metal [ 13 ]. The method of threshold selection combined with Otsu to calculate the volume distribution of nanoparticles in the brain parenchyma [ 14 ]. Otsu and multiple filtering techniques were used to detect the cracks in the concrete image [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Improvement in the Between-Class Variance Based on Lognormal Distribution for Accurate Image Segmentation

Jumiawi¹,

El-Zaart²

2022

Entropy

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There are various distributions of image histograms where regions form symmetrically or asymmetrically based on the frequency of the intensity levels inside the image. In pure image processing, the process of optimal thresholding tends to accurately separate each region in the image histogram to obtain the segmented image. Otsu’s method is the most used technique in image segmentation. Otsu algorithm performs automatic image thresholding and returns the optimal threshold by maximizing between-class variance using the sum of Gaussian distribution for the intensity level in the histogram. There are various types of images where an intensity level has right-skewed histograms and does not fit with the between-class variance of the original Otsu algorithm. In this paper, we proposed an improvement of the between-class variance based on lognormal distribution, using the mean and the variance of the lognormal. The proposed model aims to handle the drawbacks of asymmetric distribution, especially for images with right-skewed intensity levels. Several images were tested for segmentation in the proposed model in parallel with the original Otsu method and the relevant work, including simulated images and Medical Resonance Imaging (MRI) of brain tumors. Two types of evaluation measures were used in this work based on unsupervised and supervised metrics. The proposed model showed superior results, and the segmented images indicated better threshold estimation against the original Otsu method and the related improvement.

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“…Gajalakshmi et al [15] advised that the grain boundaries of various metals were determined using OTSU and Canny edge detection techniques. Chi et al [16] introduced a threshold selection method combined with OTSU to calculate the volume distribution of nanoparticles in brain parenchyma. Ahmed et al [17] detected the cracks in concrete image using OTSU and multiple filtering.…”

Section: Introductionmentioning

confidence: 99%

An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation

Zhan

Zhang

2019

Symmetry

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When using image processing technology to analyze mineral particle size in complex scenes, it is difficult to separate the objects from the background with traditional algorithms. This paper proposes an ore image segmentation algorithm based on a histogram accumulation moment, which is applied to multi-scenario ore object location and recognition. Firstly, the multi-scale Retinex color restoration algorithm is used to improve the contrast in the dark region and eliminates the shadows generated by the stacked adhesion ores. Then, the zero-order and first-order cumulative moments close to the selected gray level are calculated, reducing the error caused by noise. Finally, the selected gray level gradually approaches the optimal threshold to avoid falling into local optimum. It can segment mineral images with unimodal or insignificant bimodal characteristic histogram effectively and accurately. Ore images in three different scenarios are used to verify the accuracy and effectiveness of the proposed method. The experimental results demonstrate that the proposed algorithm provides better segmentation results than other methods.

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Improved threshold selection for the determination of volume of distribution of nanoparticles administered by convection-enhanced delivery

Cited by 6 publications

References 20 publications

Biodegradable PEG-poly(ω-pentadecalactone-co-p-dioxanone) nanoparticles for enhanced and sustained drug delivery to treat brain tumors

Biodegradable PEG-poly(ω-pentadecalactone-co-p-dioxanone) nanoparticles for enhanced and sustained drug delivery to treat brain tumors

Improvement in the Between-Class Variance Based on Lognormal Distribution for Accurate Image Segmentation

An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation

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