An Overview and Application of Deep Convolutional Neural Networks for Medical Image Segmentation

Medical imaging technologies are widely utilized as a non-invasive diagnostic process that helps professionals identify diseases and injuries. Convolutional neural networks (CNNs) have become increasingly popular since they are considered to be the best approach for a variety of computer vision problems includes medical imaging and diagnostics. Image segmentation using CNN models makes image analysis easier that can effectively use with different image modalities like X-ray, MRI, CT, Ultrasound and PET. This review article provides a perspective on using CNN models for various medical image segmentation tasks. It contains the details of benchmark deep CNN models available for image segmentation, publicly available medical imaging datasets and various applications of deep CNN models for medical image segmentation tasks.

[1]  Yu Dai,et al.  ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation , 2022, ArXiv.

[2]  Hao Wang,et al.  Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans , 2022, ArXiv.

[3]  D. Greve,et al.  An open-source tool for longitudinal whole-brain and white matter lesion segmentation , 2022, NeuroImage: Clinical.

[4]  Simone Palazzo,et al.  Hierarchical 3D Feature Learning for Pancreas Segmentation , 2021, MLMI@MICCAI.

[5]  Amjad J. Humaidi,et al.  Review of deep learning: concepts, CNN architectures, challenges, applications, future directions , 2021, Journal of Big Data.

[6]  Sanskruti Patel.  Deep Learning Models for Image Segmentation , 2021, 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom).

[7]  Hossein Khosravi,et al.  Pooling Methods in Deep Neural Networks, a Review , 2020, ArXiv.

[8]  Chandra Sekhara Rao Annavarapu,et al.  U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation , 2020, ArXiv.

[9]  Hailing Fu,et al.  A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures , 2019, Sensors.

[10]  Boudewijn P. F. Lelieveldt,et al.  Linking Convolutional Neural Networks with Graph Convolutional Networks: Application in Pulmonary Artery-Vein Separation , 2019, GLMI@MICCAI.

[11]  Toan Duc Bui,et al.  Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation , 2019, Biomed. Signal Process. Control..

[12]  Pengfei Chen,et al.  Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks , 2019, ArXiv.

[13]  Snehasis Mukherjee,et al.  Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification , 2019, Neurocomputing.

[14]  Tobias Ortmaier,et al.  A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation , 2018, International Journal of Computer Assisted Radiology and Surgery.

[15]  Meenakshi Pawar,et al.  Transfer Learning for Image Classification , 2018, 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA).

[16]  Vijayan K. Asari,et al.  Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation , 2018, ArXiv.

[17]  Sébastien Ourselin,et al.  DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Sebastian J. Schlecht,et al.  Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks , 2017, ArXiv.

[19]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[20]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  S. Venkatesh,et al.  Gender recognition from facial images using multichannel deep learning framework , 2022, Machine Learning for Biometrics.

[22]  Isha Patel,et al.  An Optimized Deep Learning Model For Flower Classification Using NAS-FPN And Faster R- CNN , 2020 .

[23]  Akshay Dudhane,et al.  Interstitial Lung Disease Classification Using Feed Forward Neural Networks , 2017 .

[24]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[25]  Neeraj Sharma,et al.  Automated medical image segmentation techniques , 2010, Journal of medical physics.