RetinAI is aware of the importance of scientific contributions to the field ophthalmology and to healthcare. In order to support the transition from reactive to precision and patient-specific medicine, and to foster the development of solutions to support patient's health and well-being, we are releasing our public peer-reviewed contributions in medical image analysis, computer vision and machine learning.

 

GLAMpoints: Greedily Learned Accurate Match points

Image registration, the process of aligning two or more images into the same global spatial reference, is a crucial task in fields like computer vision, pattern recognition and medical image analysis.

This article presents a novel CNN-based feature point detector - GLAMpoints - learned in a semi-supervised manner and trained using reinforcement learning strategies. As a result, we avoid the limitations of point matching and transformation estimation being non-differentiable.

Our detector extracts repeatable, stable interest points with a dense coverage, specifically designed to maximise the correct matching in a specific domain, in contrast to conventional techniques that optimise indirect metrics.

To illustrate the performance of our approach, we apply our method on challenging 2D retinal slit-lamp images, for which classical detectors yield unsatisfactory results due to low image quality and insufficient amount of low-level features. We show that GLAMpoints significantly outperforms classical detectors as well as state of the art CNN-based methods in matching ability and registration quality.

Authors: Truong, P., Mosinska, A., Ciller, C., Apostolopoulos, S., De Zanet, S.I.

Date: Oct. 2019, ICCV 2019 - Seoul, Korea

Link: PDF - Supplementary Material

GLAMPoints.png

Fused Detection of Retinal Biomarkers in OCT Volumes

Optical Coherence Tomography (OCT) is the primary imaging modality for detecting pathological biomarkers associated to retinal diseases such as Age-Related Macular Degeneration. In practice, clinical diagnosis and treatment strategies are closely linked to biomarkers visible in OCT volumes and the ability to identify these plays an important role in the development of ophthalmic pharmaceutical products.

In this article we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume. We do so by adding a bidirectional LSTM to fuse the outputs of a Convolutional Neural Network that predicts individual biomarkers. As a consequence, we avoid the need to use pixel-wise annotations to train our method, and instead provide fine-grained biomarker information regardless. We furthermore show that our approach imposes coherence between biomarker predictions across volume slices and our predictions are superior to several existing approaches to perform the same task.

Authors: Kurmann, T. , Márquez-Neila, P., Yu, S., Munk, M., Wolf, S. & Sznitman, R. - InnoSuisse 26362.1 PFLS-LS

Date: Oct. 2019, MICCAI, Shenzhen, China

Link: PDF

Source: Kurmann, T. et al. -  Link

Source: Kurmann, T. et al. - Link


Deep Multi Label Classification in Affine Subspaces

Multi-label classification (MLC) problems are becoming increasingly popular in the context of medical imaging. This has in part been driven by the fact that acquiring annotations for MLC is far less burdensome than for semantic segmentation and yet provides more expressiveness than multi-class classification.

However, to train MLCs, most methods have resorted to similar objective functions as with traditional multi-class classification settings. We show in this work that such approaches are not optimal and instead propose a novel deep MLC classification method in affine subspace. At its core, the method attempts to pull features of class-labels towards different affine subspaces while maximising the distance between them.

In this paper we evaluate the method using two MLC medical imaging datasets and show a large performance increase compared to previous multi label frameworks.

Authors: Kurmann, T. , Márquez-Neila, P., Wolf, S. & Sznitman, R. - InnoSuisse 26362.1 PFLS-LS

Date: Oct. 2019, MICCAI, Shenzhen, China

Link: PDF

Source: Kurmann, T. et al -  Link

Source: Kurmann, T. et al - Link


Simultaneous Classification and Segmentation of Cysts in Retinal OCT

The automatic segmentation of fluid deposits in OCT imaging enables clinically relevant quantification and monitoring of eye disorders over time. Eyes with late-stage diseases are particularly challenging to segment, as their shape is often highly warped and presents a high variability between different devices, specifications and scanning times.

In this context, the RetinAI team proposed a novel fully-Convolutional Neural Network (CNN) architecture which combines dilated residual blocks in an asymmetric U-shape configuration, and can simultaneously segment and classify cysts in pathological eyes.

This articles presents a validation of our approach on the Retouch Challenge with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) ’17 Conference dataset.

Date: Sept. 2017 - MICCAI

Link: PDF

Simultaneous_OCT.png

Pathological OCT Retinal Layer Segmentation using Branch Residual U-shape Networks - BRUNET

The automatic segmentation of retinal layer structures provides clinically-relevant quantification and monitoring of eye disorders over time in OCT. Eyes with late-stage diseases are particularly challenging to segment, as their shape is highly warped due to the presence of pathological biomarkers.

RetinAI has proposed an algorithm which combines dilated residual blocks in an asymmetric U-shape network configuration, and can segment multiple layers of highly pathological eyes in one shot. Our so called BRUnet architecture enables accurate segmentation or retinal layers by modeling the optimization as a supervised regression problem. Using lower computational resources, our strategy achieves superior segmentation performance compared to both state-of-the-art deep learning architectures and other OCT segmentation methods.

Date: Feb. 2017 - MICCAI

Link: Arxiv - PDF - MICCAI

Screenshot 2018-03-04 18.19.22.png

RetiNet: Automatic AMD identification in OCT volumetric data

Visual inspection of Optical Coherence Tomography (OCT) volumes remains the main method for AMD identification, doing so is time consuming as each cross-section within the volume must be inspected individually by the clinician. In much the same way, acquiring ground truth information for each cross-section is expensive and time consuming.

In this paper, we present a new strategy towards automatic pathology identification in OCT C-scans. By introducing a novel Convolution Neural Network (CNN) architecture, named RetiNet, that directly estimates the state of a C-scan solely using the image data and without any additional information.

Date: Oct. 2016

Link: Arxiv - PDF

Screenshot 2018-03-04 10.41.51.png
 

List of scientific publications:

2019

Truong, P., Mosinska, A., Ciller, C., Apostolopoulos, S., De Zanet, S.I. GLAMpoints: Greedily Learned Accurate Match points, ICCV, Seoul - Korea, November 2019

Marquez-Neila, P. & Sznitman, R., Image data validation for medical systems, MICCAI 2019 Shenzhen, China, October 2019

Kurmann, T. , Márquez-Neila, P., Yu, S., Munk, M., Wolf, S. & Sznitman, R., Fused Detection of Retinal Biomarkers in OCT Volumes, MICCAI 2019 Shenzhen, China, October 2019

Kurmann, T. , Márquez-Neila, P., Wolf, S. & Sznitman, R., Deep Multi Label Classification in Affine Subspaces, MICCAI 2019 Shenzhen, China, October 2019

Bogunović, H. , Venhuizen, F., Klimscha, S. , Apostolopoulos, S. et al. RETOUCH-The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge, IEEE Transactions on Medical Imaging, February 2019

Giannakaki-Zimmermann, H., Huf, W., Schaal, K.B., Schürch, K., Dysli, C., Dysli, M., Zenger, A., Ceklic, L., Ciller, C., Apostolopoulos, S., De Zanet, S., Sznitman, R., Ebneter, A., Zinkernagel, MS., Wolf, S., Munk, M., Comparison of choroidal thickness measurements using spectral domain optical coherence tomography in six different settings and with customised automated segmentation, Translational Vision Science & Technology, May 2019

2017

Ciller, C., De Zanet, S. et al. Multi-channel MRI segmentation of eye structures and tumors using patient specific eye features, PlosOne, 2017

Ciller, C., De Zanet, S., Apostolopoulos, S. et al. Automatic Segmentation of Retinoblastoma in Fundus Image Photography using Convolutional Neural Networks, ARVO 2017, Baltimore 

Apostolopoulos, S., De Zanet, S., Ciller, C. et al. Pathological OCT Retinal Layer Segmentation Using Branch Residual U-style Networks, MICCAI Quebec & Arxiv, 2017

Apostolopoulos, S. et al. Efficient OCT volume reconstruction from slit lamp microscopes, IEEE TBME, 2017

2016

Apostolopoulos, S., Ciller, C., De Zanet, S. et al. RetiNet: Automatic AMD identification in OCT volumetric data, Arxiv, 2016

De Zanet, S. et al. Retinal slit lamp video mosaicking. International Journal of Computer Assisted Radiology and Surgery, International Journal of Computer Assisted Radiology and Surgery, 2016

2015

De Zanet, S. , Ciller, C. et al. Landmark Detection for Fusion of Fundus and MRI Toward a Patient Specific Multi-modal Eye Model, IEEE TBME, 2015

Ciller, C., De Zanet, S. et al. Automatic Segmentation of the eye in 3D MRI: A novel statistical shape model for treatment planning of retinoblastoma, Int. J. Radiation Oncology BiologyPhysics (Red Journal), 2015