OCT-seg-papers

Personal paper/code/dataset list on OCT segmentation

Background

• 2018 Prevalences of segmentation errors and motion artifacts in OCT-angiography differ among retinal diseases
  https://link.springer.com/article/10.1007/s00417-018-4053-2 Bscan + xy coordinates input, pixel label + column wise softmax for boundary

• 2013 A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina
  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3785070/

Deep Learning

• 2021 (He et al) Structured layer surface segmentation for retina OCT using fully convolutional regression networks
  https://www.sciencedirect.com/science/article/pii/S1361841520302206 \

• 2021 (MGU-Net) Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images
  https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-12-4-2204&id=449401
  code: https://github.com/Jiaxuan-Li/MGU-Net
  dataset: http://www.yuyeling.com/project/mgu-net/

• 2021 Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy
  https://doi.org/10.1016/j.compbiomed.2021.104727
  recurrent ASPP based deep encoder-decoder network, Dice loss function Fluid only, why 3 channel input??

• 2019 (He et al) Fully Convolutional Boundary Regression for Retina OCT Segmentation
  https://link.springer.com/chapter/10.1007/978-3-030-32239-7_14
  preprocessing code: https://github.com/heyufan1995/oct_preprocess
  2D Unet, column wise softmax for boundary

• 2020 (Google DeepMind) Predicting conversion to wet age-related macular degeneration using deep learning
  https://rdcu.be/b4fgc

• 2018 (Google DeepMind) Clinically applicable deep learning for diagnosis and referral in retinal disease
  https://rdcu.be/4sNU 9 neighboring B-scans for central B-scan segmentation

• 2017 ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks
  https://arxiv.org/abs/1704.02161
  code: https://github.com/ai-med/relaynet_pytorch

• 2017 Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search
  https://www.osapublishing.org/DirectPDFAccess/C104984E-97A7-91AD-422CBA1DAF54A23D_363511/boe-8-5-2732.pdf?da=1&id=363511&seq=0&mobile=no

• AI challenge Unet DME segmentation
  https://github.com/ShawnBIT/AI-Challenger-Retinal-Edema-Segmentation

• caffe code: https://github.com/SJD095/OCT-Segmentation

Conventional

• 2018 A Supervised Joint Multi-layer Segmentation Framework for Retinal Optical Coherence Tomography Images using Conditional Random Field
  https://www.sciencedirect.com/science/article/pii/S0169260717314645
  code: https://github.com/arunava555/OCT-layer-segmentation
  CRF, code in Matlab

• Segmentation and analysis of retinal layers from individual OCT b-scans
  code: https://github.com/sarastokes/OCT-tools

• 2014 Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization
  http://www.sciencedirect.com/science/article/pii/S1361841514000449
  code: https://github.com/FabianRathke/octSegmentation
  (working version https://github.com/yiqian-wang/octSegmentation)
  probabilistic graphical model, 9 boundaries, 2D and 3D

Dataset

• 2021 (MGU-Net) Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images
  dataset: http://www.yuyeling.com/project/mgu-net/

• 2019 RETOUCH: the retinal OCT fluid detection and segmentation benchmark and challenge https://ieeexplore.ieee.org/document/8653407
  https://retouch.grand-challenge.org

• 2019 OCTID: Optical coherence tomography image database https://arxiv.org/ftp/arxiv/papers/1812/1812.07056.pdf \

• 2018 Identifying medical diagnoses and treatable diseases by image-based deep learning https://www.sciencedirect.com/science/article/pii/S0092867418301545 \

• 2014 Diabetic macular edema dataset
  http://people.duke.edu/~sf59/Chiu_BOE_2014_dataset.htm
  preprocessing code: https://github.com/heyufan1995/oct_preprocess

• Healthy and Multiple Sclerosis dataset
  http://iacl.ece.jhu.edu/index.php?title=Resources

• Retinal-OCT-Images
  https://github.com/lidaboo/Retinal-OCT-Images

Other Resources

• OCT converter
  https://github.com/marksgraham/OCT-Converter

• Heidelberg .vol Reader
  https://github.com/ayl/heyexReader

• LibOCTData \ https://github.com/neurodial/LibOctData