# The Active Segmentation Platform for Microscopic Image Classification and Segmentation
## 1 Introduction  
Segmentation of cells and sub-cellular structures (e.g., organelles) is a frequent intermediate step in cell biology and pathology [1, 2]. For example, in digital pathology, detection and segmentation of nuclei is one of the core operations in image analysis [3]. Similar is the situation in plant microscopy [4]. Image segmentation can be broadly defined as isolating objects of interest from an image by either a manual or automatic algorithmic labeling. Automated approaches for cell segmentation have a long history, without ever reaching a universal solution [1]. This is far from a surprise, because the underlying physical modalities are very diverse. Sample preparation can be also quite different, depending on the particular experimental question--i.e., light microscopy using colored histological stains, which is still the standard in pathology; vs. fluorescent microscopy using various fluorescent stains or genetic fluorescent markers, such as the green fluorescent protein GFP; vs. transmission electron microscopy (TEM) using heavy metal contrasts (for example osmium) for sub cellular structures; vs. phase contrast microscopy for live cell imaging. The majority of traditional cell segmentation methods are based on only a few basic approaches. Following in part Mejiering [1] these can be classified into:  
* histogram based
* edge-based
* morphological operations
* region-based, such as watersheds
* deformable model fitting  
Algorithms tailored for one imaging modality are very likely to fail in other modalities. For example, intensity thresholding completely fails if there is a large intensity variation of the image background, for example in a dense tissue sample. Various pre-processing steps, such as morphological top-hats of high-pass filtering can be implemented, but they are only suitable for images, which are sufficiently similar to the test batches for which the pre-processing is optimized [5]. On the other hand, the manual segmentation is labor intensive and prone to intersession and inter-observer variations. The segmentation of microscopic images is a non-trivial problem. While in the past such segmentation was performed by experts, the demands and intensity of the present experimental approaches makes such an approach non-viable. While simple protocols, such as cell counting (e.g., in a live/dead assay), can be addressed adequately by approximate segmentation, this is far from a complete morphological characterization, where the shape and orientation are to be quantified. Cells frequently have complex shapes, examples here include astrocytes, microglia, dendritic cells, etc. On the second place, in some cases, structures within some cell types can be of interest--i.e., mitochondria, synaptic vesicles, nuclei etc. As an example of the traditional approach can be identified NucleusJ [6]. It has a specific application targeting nuclear segmentation, morphometry and statistical analysis of the nucleus. The question then arises how can the expert knowledge be harnessed by computer algorithms in the best way in order to develop versatile segmentation solutions able to address a wide range of segmentation problems. Ideally, such an approach would be  
1. deterministic, in order to ensure reproducibility of the results;
2. providing an uncertainty measure of the segmentation outcome;
3. able to provide some insight into how the morphology is objectively described
4. able to learn new information whenever such is presented  
At present, there are several available platforms covering parts of the above requirements --Trainable Weka Segmentation (TWS) [7] (part of Fiji), Ilastik [8], Icy [9] and CellProfiler [10]. An interesting development is the QuPath platform [11]. It targets digital pathology as a main application. The platform was specifically designed to analyze and explore whole slide imaging data consisting of image tiles. This is, of course, a different path compared to ImageJ and Ilastik, where the planar image is a compact object.