Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Berral, Josep Lluís; Oriol, Aranda,; Domínguez, Juan Luis; Torres, Jordi

doi:10.1109/ipdpsw55747.2022.00172

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Each image has 240 × 240 × 155 volume size, and has uniform, 1 mm 3 , voxel resolution. A sample of the dataset before preprocessing is shown in Figure 1 (adopted from [59]).…”

Section: Datasetmentioning

confidence: 99%

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Yilmaz

Akdag²,

Dalveren

et al. 2023

Diagnostics

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Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

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“…Each image has 240 × 240 × 155 volume size, and has uniform, 1 mm 3 , voxel resolution. A sample of the dataset before preprocessing is shown in Figure 1 (adopted from [59]).…”

Section: Datasetmentioning

confidence: 99%

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Yilmaz

Akdag²,

Dalveren

et al. 2023

Diagnostics

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Automated Tumor Segmentation in Radiotherapy

Savjani

Lauria²,

Bose³

et al. 2022

Seminars in Radiation Oncology

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Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images

Jakhongir

Abdusalomov

Whangbo

2022

Sensors

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Among researchers using traditional and new machine learning and deep learning techniques, 2D medical image segmentation models are popular. Additionally, 3D volumetric data recently became more accessible, as a result of the high number of studies conducted in recent years regarding the creation of 3D volumes. Using these 3D data, researchers have begun conducting research on creating 3D segmentation models, such as brain tumor segmentation and classification. Since a higher number of crucial features can be extracted using 3D data than 2D data, 3D brain tumor detection models have increased in popularity among researchers. Until now, various significant research works have focused on the 3D version of the U-Net and other popular models, such as 3D U-Net and V-Net, while doing superior research works. In this study, we used 3D brain image data and created a new architecture based on a 3D U-Net model that uses multiple skip connections with cost-efficient pretrained 3D MobileNetV2 blocks and attention modules. These pretrained MobileNetV2 blocks assist our architecture by providing smaller parameters to maintain operable model size in terms of our computational capability and help the model to converge faster. We added additional skip connections between the encoder and decoder blocks to ease the exchange of extracted features between the two blocks, which resulted in the maximum use of the features. We also used attention modules to filter out irrelevant features coming through the skip connections and, thus, preserved more computational power while achieving improved accuracy.

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Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Cited by 3 publications

References 32 publications

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Automated Tumor Segmentation in Radiotherapy

Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images

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