Introducing Soft Topology Constraints in Deep Learning-based Segmentation using Projected Pooling Loss

International audienceDeep learning methods have achieved impressive results for 3D medical image segmentation. However, when the network is only guided by voxel-level information, it may provide anatomically aberrant segmentations. When performing manual segmentations, experts heavily rely on prior anatomical knowledge. Topology is an important prior information due to its stability across patients. Recently, several losses based on persistent homology were proposed to constrain topology. Persistent homology offers a principled way to control topology. However, it is computationally expensive and complex to implement, in particular in 3D. In this paper, we propose a novel loss function to introduce topological priors in deep learning-based segmentation, which is fast to compute and easy to implement. The loss performs a projected pooling within two steps. We first focus on errors from a global perspective by using 3D MaxPooling to obtain projections of 3D data onto three planes: axial, coronal and sagittal. Then, 2D MaxPooling layers with different kernel sizes are used to extract topological features from the multi-view projections. These two steps are combined using only MaxPooling, thus ensuring the efficiency of the loss function. Our approach was evaluated in several medical image datasets (spleen, heart, hippocampus, red nucleus). It allowed reducing topological errors and, in some cases, improving voxel-level accuracy

Active contour method for ILM segmentation in ONH volume scans in retinal OCT

The optic nerve head (ONH) is affected by many neurodegenerative and autoimmune inflammatory conditions. Optical coherence tomography can acquire high-resolution 3D ONH scans. However, the ONH's complex anatomy and pathology make image segmentation challenging. This paper proposes a robust approach to segment the inner limiting membrane (ILM) in ONH volume scans based on an active contour method of Chan-Vese type, which can work in challenging topological structures. A local intensity fitting energy is added in order to handle very inhomogeneous image intensities. A suitable boundary potential is introduced to avoid structures belonging to outer retinal layers being detected as part of the segmentation. The average intensities in the inner and outer region are then resealed locally to account for different brightness values occurring among the ONH center. The appropriate values for the parameters used in the complex computational model are found using an optimization based on the differential evolution algorithm. The evaluation of results showed that the proposed framework significantly improved segmentation results compared to the commercial solution

Neue Methoden der Nachbearbeitung und Analyse retinaler optischer Kohärenztomografieaufnahmen bei neurologischen Erkrankungen

Viele neurologische Krankheiten verursachen Veränderungen in der Netzhaut, die mit Hilfe der optischen Kohärenztomography (optical coherence tomography, OCT) dargestellt werden können. Dabei entstehen viele Bilddaten, deren Auswertung zeitintensiv ist und geschultes Personal erfordert. Ziel dieser Arbeit war die Entwicklung neuer Methoden zur Vorverarbeitung und Analyse retinaler OCT-Bilddaten, um Outcome-Parameter für Studien und diagnostische Marker für neurologische Erkrankungen zu verbessern. Dazu wurden Methoden für zwei wichtige Aufnahmebereiche der Netzhaut, den Sehnervenkopf (optic nerve head, ONH) und die Makula, entwickelt. Für den ONH-Bereich wurde eine automatische Segmentierung auf Basis aktiver Konturen entwickelt, die eine akkurate Segmentierung der inneren Grenzmembran auch bei komplexer Topografie ermöglicht. Für den Bereich um die Makula entstand eine intraretinale Schichtensegmentierungspipeline, die von der Auswahl der Bilddaten über die automatische Segmentierung sowie die manuelle Nachkorrektur bis zur Ausgabe verschiedener Schichtdicken in Tabellenform reicht. Für beide Aufnahmebereiche wurden mehrere Programme entwickelt, die auf einer gemeinsamen Basis zur Verarbeitung von OCT-Daten fußen. Eines dieser Programme bietet eine grafische Oberfläche zur manuellen Verarbeitung der Bilddaten. Mit dieser Software wurden Teile der Referenzdaten manuell erstellt, die innere Grenzmembran des ONH automatisch segmentiert sowie eine komfortable Nachbearbeitung von intraretinalen Segmentierungen vorgenommen. Dies ermöglichte die automatische Auswertung morphologischer Parameter des ONH, wovon einige signifikante Unterschiede zwischen Patienten mit neurologischen Krankheiten und gesunden Kontrollen zeigten. Weiter kam die Schichtensegmentierungspipeline beim Aufbau einer normativen Datenbank sowie in einer Studie zum Zusammenhang des retinalen Schadens mit der kritischen Flimmerfrequenz zum Einsatz. Ein Teil der Software wurde als freie und quelloffene Software (free and open-source software, FOSS) und der normative Datensatz für die Verwendung in anderen Studien freigegeben. Beides wird bereits in weiteren Studien eingesetzt und wird auch die Durchführung zukünftiger Studien vereinfachen sowie die Entwicklung neuer Methoden unterstützen.Many neurological diseases cause changes in the retina, which can be visualized using optical coherence tomography (OCT). This process produces large amounts of image data. Its evaluation is time-consuming and requires medically trained personnel. This dissertation aims to develop new methods for preprocessing and analyzing retinal OCT data in order to improve outcome parameters for clinical studies and diagnostic markers for neurological diseases. For this purpose, methods concerning the regions of two landmarks of the retina, the optic nerve head (ONH) and the macula, were developed. For the ONH, an automatic segmentation method based on active contours was developed, which allows accurate segmentation of the inner limiting membrane even in complex topography. For the macular region, an intraretinal layer segmentation pipeline from image data via automatic segmentation to manual post-correction and the output of different layer thicknesses in tabular form was developed. For both, ONH and macular region, several programs were developed, which share a common basis for processing OCT data. One of these programs offers a graphical user interface for the manual processing of image data. Parts of the reference data were created manually using this software. Moreover, the inner limiting membrane of the ONH was segmented automatically and post-processing of intraretinal segmentations was performed. This allowed for automatic evaluation of morphological parameters of the ONH, some of which showed significant differences between patients with neurological diseases and the healthy control group. Furthermore, the layer segmentation pipeline was utilized to create a normative database as well as to investigate the correlation of retinal damage and critical flicker frequency. Part of the software was released as free and open-source software (FOSS) and the normative data set was released for use in other studies. Both are already being used in further studies and will also aid in future studies, as well as support the development of new methods