YOLO and Morphing-based Method for 3D Individualised Bone Model Creation

Krawczyk, Zuzanna; Starzyński, Jacek

doi:10.1109/ijcnn48605.2020.9206783

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…In further research the authors would like to incorporate the U-net-Resnet based segmentation step into the automatic bones model creation framework [7]. The prior step will include enriching the training data set by providing additional images of the pelvis bone and upper part of the femur bone -thus they are under-represented and more anatomically complex in comparison to the images of the lower part of the femur.…”

Section: Discussionmentioning

confidence: 99%

“…The aim of this paper is the verification of usefulness of popular deep model networks, used for general purpose segmentation to the task of bone structure segmentation out of CT data series. The automatic deep learning based bone segmentation can be further used in the method of automatic femur bone creation described in [7], replacing its current bone edge detection phase, based on state of the art image processing algorithms. Four models of neural networks are compared: FCN [8], PSPNet [9], U-net [10] and Segnet [11].…”

Section: Introductionmentioning

confidence: 99%

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Krawczyk

Starzyński

2021

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chapter presents the results obtained. The last chapter focuses on conclusions and perspectives for further work.The preliminary results of the work were presented in the CPEE 2020 conference [12]. The paper broadens the subject with segmentation performed by FCN network and modifications of the U-net network. Evaluation of the results was performed on much larger dataset with the use of additional fitting measures. Literature review and goalRecent publications reported that deep neural networks are capable to perform segmentation task on medical images with the accuracy comparable to human expert. One of the common tasks performed successfully by the deep neural network is organ segmentation. In [13] novel superpixel-based and boundary sensitive CNN was used for liver segmentation out of CT data and achieved high Dice coefficient value equal to 97.31 ± 0.36%. Authors of [14] successfully performed segmentation of head and neck organs at risks utilising 3D U-net architecture with performance similar to the expert.The segmentation process is also used in detection of pathological changes in the human body. Authors of [15] performed segmentation of polycystic kidneys with mean volume difference 0.68 ± 2.2.% in comparison to expert segmentation. Various deep convolutional neural networks are also utilised to the task of automatic brain tumour segmentation [16].In addition to the CT and MRI imaging analysis, deep neural networks are also used in the processing of histopathological images for example in segmenting and classifying epithelial and stromal regions [17] or in damaged tissue detection [18].Several papers tackled the subject of bone structure segmentation. In [19] a custom deep learning method was developed for segmentation of 49 bones out of PET/CT datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Krawczyk

Starzyński

2021

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“…Our unique annotated MRI pictures were used to train this model efficiently. YOLO is a single convolutional neural network, unlike other neural network-based frameworks for object identification [8,9]. It has two fully connected layers for bounding box prediction and 24 convolutional layers for extracting information from pictures.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

Shelatkar

Garg

Shorfuzzaman

et al. 2022

Computational and Mathematical Methods in Medicine

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Brain cancer is a rare and deadly disease with a slim chance of survival. One of the most important tasks for neurologists and radiologists is to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can diagnose brain tumors by employing magnetic resonance imaging (MRI) as a supporting technology. We propose transfer learning approaches for a deep learning model to detect malignant tumors, such as glioblastoma, using MRI scans in this study. This paper presents a deep learning-based approach for brain tumor identification and classification using the state-of-the-art object detection framework YOLO (You Only Look Once). The YOLOv5 is a novel object detection deep learning technique that requires limited computational architecture than its competing models. The study used the Brats 2021 dataset from the RSNA-MICCAI brain tumor radio genomic classification. The dataset has images annotated from RSNA-MICCAI brain tumor radio genomic competition dataset using the make sense an AI online tool for labeling dataset. The preprocessed data is then divided into testing and training for the model. The YOLOv5 model provides a precision of 88 percent. Finally, our model is tested across the whole dataset, and it is concluded that it is able to detect brain tumors successfully.

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Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine Tuning Approach

Shelatkar,

Bansal

2023

Lecture Notes in Electrical Engineering

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YOLO and Morphing-based Method for 3D Individualised Bone Model Creation

Cited by 6 publications

References 26 publications

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine Tuning Approach

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