Serial section electron microscopy (SSEM) image stacks generated using high throughput microscopy techniques are an integral tool for investigating brain connectivity and cell morphology. FIB or 3View scanning electron microscopes easily generate gigabytes of data. In order to produce analyzable 3D dataset from the imaged volumes, efficient and reliable image segmentation is crucial. Classical manual approaches to segmentation are time consuming and labour intensive. Semiautomatic seeded watershed segmentation algorithms, such as those implemented by ilastik image processing software, are a very powerful alternative, substantially speeding up segmentation times. We have used ilastik effectively for small EM stacks – on a laptop, no less; however, ilastik was unable to carve the large EM stacks we needed to segment because its memory requirements grew too large – even for the biggest workstations we had available. For this reason, we refactored the carving module of ilastik to scale it up to large EM stacks on large workstations, and tested its efficiency. We modified the carving module, building on existing blockwise processing functionality to process data in manageable chunks that can fit within RAM (main memory). We review this refactoring work, highlighting the software architecture, design choices, modifications, and issues encountered.
|Original language||English (US)|
|Title of host publication||2016 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|State||Published - Dec 1 2016|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by CRG3 KAUST grant