UNet Architectures in Multiplanar Volumetric Segmentation -- Validated on Three Knee MRI Cohorts

UNet has become the gold standard method for segmenting 2D medical images that any new method must be validated against. However, in recent years, several variations of the seminal UNet have been proposed with promising results. However, there is no clear consensus on the generalisability of these architectures, and UNet currently remains the methodological gold standard. The purpose of this study was to evaluate some of the most promising UNet-inspired architectures for 3D segmentation. For the segmentation of 3D scans, UNet-inspired methods are also dominant, but there is a larger variety across applications. By evaluating the architectures in a different dimensionality, embedded in a different method, and for a different task, we aimed to evaluate if any of these UNet-alternatives are promising as a new gold standard that generalizes even better than UNet. Specifically, we investigated the architectures as the central 2D segmentation core in the Multi-Planar Unet 3D segmentation method that previously demonstrated excellent generalization in the MICCAI Segmentation Decathlon. Generalisability can be demonstrated if a promising UNet-variant consistently outperforms UNet in this setting. For this purpose, we evaluated four architectures for cartilage segmentation from three different cohorts with knee MRIs.

One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation

Many recent medical segmentation systems rely on powerful deep learning models to solve highly specific tasks. To maximize performance, it is standard practice to evaluate numerous pipelines with varying model topologies, optimization parameters, pre- & postprocessing steps, and even model cascades. It is often not clear how the resulting pipeline transfers to different tasks. We propose a simple and thoroughly evaluated deep learning framework for segmentation of arbitrary medical image volumes. The system requires no task-specific information, no human interaction and is based on a fixed model topology and a fixed hyperparameter set, eliminating the process of model selection and its inherent tendency to cause method-level over-fitting. The system is available in open source and does not require deep learning expertise to use. Without task-specific modifications, the system performed better than or similar to highly specialized deep learning methods across 3 separate segmentation tasks. In addition, it ranked 5-th and 6-th in the first and second round of the 2018 Medical Segmentation Decathlon comprising another 10 tasks. The system relies on multi-planar data augmentation which facilitates the application of a single 2D architecture based on the familiar U-Net. Multi-planar training combines the parameter efficiency of a 2D fully convolutional neural network with a systematic train- and test-time augmentation scheme, which allows the 2D model to learn a representation of the 3D image volume that fosters generalization.