Model-Guided Segmentation of Liver in CT and PET-CT Images of Child Patients Based on Statistical Region Merging

Sedlář, Jiří; Bajger, Mariusz; Caon, Martin

doi:10.1109/dicta.2016.7797064

Cited by 3 publications

(2 citation statements)

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“…It is also possible to use iterative searches on certain image structures to determine characteristics and ROI. Statistical methods are also used in which the image is navigated from previous information of characteristic behaviors [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of deep neural network architectures in the identification of bone fissures

2020

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Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.

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Section: Introductionmentioning

confidence: 99%

Evaluation of deep neural network architectures in the identification of bone fissures

2020

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“…The use of medical image processing as a help for medical diagnosis, presents a number of applications [1], [2], some of which have a determined ROI (region of interest) [3], [4], while others try to get to determine this region using different segmentation techniques [5], some of these techniques use characteristic extraction by iterative search of certain structures [6], some others seek certain type of fissures, cracks or ruptures in structures to analyze [3], others look through statistical methods [7] regions or characteristics to analyze. At the same time, behavioral analysis of certain organs, muscle and bone structures, dental [8] can be performed using non-invasive methods [9], taking the information from electrodes, resonances and digitalized radiographs.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Fissures in Bony Structures through Image Processing

Ariza¹,

Jacinto²,

Sarmiento³

2018

IJET

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This document shows the design of a software for the detection of very pronounced edges, fissures and fractures of bone structures in radiological images, using image processing techniques as an enhancement method. A graphic interface was used for the user to select the bone to inspect, by means of an object labeling algorithm, the coordinates of said bone, "region of interest" ROI are identified, after which the thresholding of the image will be performed, then a series of morphological operations will be applied and in this way the detection of the lesion, which is highlighted by the change of color for the visualization in the output image, making an overlap of the fissures and / or fractures in the original image.

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Paediatric Liver Segmentation for Low-Contrast CT Images

Bajger

Caon

2018

Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

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Model-Guided Segmentation of Liver in CT and PET-CT Images of Child Patients Based on Statistical Region Merging

Cited by 3 publications

References 20 publications

Evaluation of deep neural network architectures in the identification of bone fissures

Evaluation of deep neural network architectures in the identification of bone fissures

Recognition of Fissures in Bony Structures through Image Processing

Paediatric Liver Segmentation for Low-Contrast CT Images

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