Medical image segmentation is about partitioning a medical image into multiple segments or regions, each segmentation or region composed of a set of pixels or voxels. Often, segments correspond to semantically meaningful anatomical objects.[1]
| Title | Date | Links | First Author | Code |
|---|---|---|---|---|
| A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises | 2020 | CORR | S.Kevin Zhou | No |
| A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis | 2020 | CoRR | Xiaozheng Xie | No |
| Model-Based and Data-Driven Strategies in Medical Image Computing | 2019 | Proceedings of the IEEE | Daniel Rueckert | No |
| Deep neural network models for computational histopathology: A survey | 2019 | CoRR | Chetan L.Srinidhi | No |
| Deep Learning in Medical Ultrasound Analysis: A Review | 2019 | Engineering | Shenfeng Liu | No |
| Generative Adversarial Network in Medical Imaging: A Review | 2018 | Medical Image Analysis | Xin Yi | No |
| GANs for Medical Image Analysis | 2018 | CoRR | Salome Kazeminia | No |
| Deep learning in medical imaging and radiation therapy | 2018 | Medical Physics | Berkman Sahiner | No |
| Deep Learning in Microscopy Image Analysis: A Survey | 2017 | IEEE Transactions on Neural Networks and Learning Systems | Fuyong Xing | No |
| Deep Learning in Medical Image Analysis | 2017 | Annual Review of Biomedical Engineering | Dinggang Shen | No |
| Deep Learning Applications in Medical Image Analysis | 2017 | IEEE Access | Justin Ker | No |
| A survey on deep learning in medical image analysis | 2017 | Medical Image Analysis | Greet Litjens | No |
| Overview of deep learning in medical imaging | 2017 | Radiological Physics and Technology | Kenji Suzuki | No |
| Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique | 2016 | IEEE Transactions on Medical Imaging | Hayit Greenspan | No |
| Title | Date | Links | First Author | Code |
|---|---|---|---|---|
| A survey on U-shaped networks in medical image segmentations | 2020 | Neurocomputing | Liangliang Liu | No |
| Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges | 2019 | Journal of Digital Imaging | Mohammad Hesam Hesamian | No |
| Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation | 2019 | Medical Image Analysis | Nima Tajbakhsh | No |
| Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications | 2019 | CoRR | Hyunseok Seo | No |
| Title | Date | Links | First Author | Code |
|---|---|---|---|---|
| Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study | 2021 | arxiv | K. E. Sengun | --- |
| Title | Date | Links | First Author | Model+Code |
|---|---|---|---|---|
| PAIP 2019: Liver cancer segmentation challenge | 2021 | Medical Image Analysis | Yoo Jung Kim | -- |
| Weakly-Supervised Teacher-Student Network for Liver Tumor Segmentation from Non-enhanced Images | 2021 | Medical Image Analysis | Dong Zhang | --- |
| Hybrid Cascaded Neural Network for Liver Lesion Segmentation | 2020 | ISBI | Raunak Dey | Cascaded neural network |
| Liver Segmentation in CT with MRI Data: Zero-Shot Domain Adaptation by Contour Extraction and Shape Priors | 2020 | ISBI | Pham | U-net |
| Mask Mining for Improved Liver Lesion Segmentation | 2020 | ISBI | Karsten Roth | U-net |
| Training Liver Vessel Segmentation Deep Neural Networks on Noisy Labels from Contrast CT Imaging | 2020 | ISBI | Minfeng Xu | CNN |
| Deep Learning and Unsupervised Fuzzy C-Means Based Level-Set Segmentation for Liver Tumor | 2020 | ISBI | Yue Zhang | level-set |
| Liver Guided Pancreas Segmentation | 2020 | ISBI | Yue Zhang | 3D CNN |
| Feature Fusion Encoder Decoder Network for Automatic Liver Lesion Segmentation | 2019 | ISBI | Xueying Chen | FED-Net |
| Liver Steatosis Segmentation With Deep Learning Methods | 2019 | ISBI | Xiaoyuan Guo | Mask-RCNN |
| A Controlled Generative Model for Segmentation of Liver Tumors | 2019 | ICEE | Nasim Nasiri | Generative Model |
| Radiomics-guided GAN for Segmentation of Liver Tumor Without Contrast Agents | 2019 | MICCAI | Xiaojiao Xiao | GAN |
| Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation | 2019 | MICCAI | Junlin Yang | DADR |
| Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks | 2019 | MICCAI | Qi Zeng | FCN |
| Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior | 2019 | MICCAI | Han Zheng | DAP |
| Liver lesion segmentation informed by joint liver segmentation | 2018 | ISBI | Eugene Vorontsov | FCN |
| Automatic Liver Segmentation Using an Adversarial Image-to-Image Network | 2017 | MICCAI | Dong Yang | DI2IN |
| Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields | 2016 | MICCAI | Patrick Ferdinand Christ | Cascade FCN |
| Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method | 2017(ISBI Rank first) | CoRR | Xiao Han | U-Net+Resnet 3D |
| 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes | 2016 | MICCAI | Qi Dou | 3D |
| Automatic 3D liver location and segmentation via convolutional neural networks and graph cut | 2016 | CoRR | Fang Lu | 3D |
| 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes | 2016 | MICCAI | Qi Dou | 3D DSN |
| Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced CT scans | 2016 | ISBI | P.-H. Conze | RF |
| Date | First Author | Methods | N Dimension | Liver Per Case Dice | Liver Global Dice | Tumor Per Case Dice | Tumor Global Dice | Links |
|---|---|---|---|---|---|---|---|---|
| 202004 | Fabian Isensee | UNet | 2D, 3D | 0.967 | 0.970 | 0.763 | 0.858 | Automated Design of Deep Learning Methods for Biomedical Image Segmentation (arxiv) |
| 201909 | Xudong Wang | 3D | - | - | 0.741 | - | Volumetric Attention for 3D Medical Image Segmentation and Detection (MICCAI2019) | |
| 201908 | Jianpeng Zhang | 3D | 0.965 | 0.968 | 0.730 | 0.820 | Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation (IJCAI 2019) | |
| 202007 | Youbao Tang | E^2Net | 0.966 | 0.968 | 0.724 | 0.829 | E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans (arXiv) | |
| 201709 | Xiaomeng Li | H-DenseUNet | 0.961 | 0.965 | 0.722 | 0.824 | H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes, (TMI), (Keras code) |
| Date | First Author | Network Architecture | N Dimension | Liver Per Case Dice | Liver Global Dice | Tumor Per Case Dice | Tumor Global Dice | Links |
|---|
| Methods | Scholars | Date | Contens | Code |
|---|---|---|---|---|
| NiftyNet | E. Gibson | 2020 | (1)Support for 2-D, 2.5-D, 3-D, 4-D inputs (2)Implementation of recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic) (3)Comprehensive evaluation metrics for medical image segmentation | Tensorfolw |
| MIScnn | Dominik Müller | 2019 | A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning. | keras |
| segmentation_models | Pavel Yakubovskiy | 2019 | Python library with Neural Networks for Image Segmentation based on PyTorch. | Pytorch |
Part Eight: Liver and Tumor Datasets
| Dataset | Date | Paper |
|---|---|---|
| MICCAI-SLiver07 | 2007 | IEEE TMI |
| Lits-2017 | 2017 | -- |
| Title | Date | Links | First Author | Code |
|---|
1) Zhou S K . Introduction to Medical Image Recognition, Segmentation, and Parsing[M]// Medical Image Recognition, Segmentation and Parsing. 2016.