CELL SEGMENTATION AND TRACKING VIA PROPOSAL GENERATION AND SELECTION

Biology and medicine rely heavily on images to understand how the body functions, for
diagnosing diseases and to test the effects of treatments. In recent decades, microscopy has
experienced rapid improvements, enabling imaging of fixed and living cells at higher resolutions
and frame rates, and deeper inside the biological samples. This has led to rapid growth in the image
data. Automated methods are needed to quantitatively analyze these huge datasets and find
statistically valid patterns. Cell segmentation and tracking is critical for automated analysis, yet it
is a challenging problem due to large variations in cell shapes and appearances caused by various
factors, including cell type, sample preparation and imaging setup.
This thesis proposes novel methods for segmentation and tracking of cells, which rely on
machine learning based approaches to improve the performance, generalization and reusability of
automated methods. Cell proposals are used to efficiently exploit spatial and temporal context for
resolving detection ambiguities in high-cell-density regions, caused by weak boundaries and
deformable shapes of cells. This thesis presents two cell proposal methods: the first method uses
multiple blob-like filter banks for detecting candidates for round cells, while the second method,
Cell Proposal Network (CPN), uses convolutional neural networks to learn the cell shapes and
appearances, and can propose candidates for cells in a wide variety of microscopy images. CPN
first regresses cell candidate bounding boxes and their scores, then, it segments the regions inside
the top ranked boxes to obtain cell candidate masks. CPN can be used as a general cell detector,
as is demonstrated by training a single model to segment images from histology, fluorescence and
phase-contrast microscopy.
This work poses segmentation and tracking as proposal selection problems, which are solved
optimally using integer linear programming or approximately using iterative shortest cost path
search and non-maximum suppression. Additionally, this thesis presents a method which utilizes
graph-cuts and an off-the-shelf edge detector to accurately segment highly deformable cells.
The main contribution of this thesis is a cell tracking method which uses CPN to propose cell
candidates, represents alternative tracking hypotheses using a graphical model, and selects the
globally optimal sub-graph providing cell tracks. It achieves state-of-the-art tracking performance
on multiple public benchmark datasets from both phase-contrast and fluorescence microscopy
containing cells of various shapes and appearances.


ISBN-10:
9789526217284
Julkaisusarja:
ACTA UNIVERSITATIS OULUENSIS C Technica
Kustantaja:
Oulun yliopisto
Painos:
Osajulkaisuväitöskirjan yhteenveto-osa
Painovuosi:
2017
Sivumäärä:
96
Tekijät:
AKRAM SAAD ULLAH
Viivakoodi 9789526217284
Tuotekoodi 024635
Tekijä AKRAM SAAD ULLAH
18,00 €