ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

Abstract

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

The critical balance between dopamine D2 receptor and RGS for the sensitive detection of a transient decay in dopamine signal

“…AC1 and AC5 are both expressed in the cortex and hippocampus [ 16 ]. However, these regions only show weak DA signal because of the small numbers of DA fibers [ 93 , 94 ], and the DA signal likely plays more modulatory roles with longer time constants (~10 min) [ 6 , 95 ]. Rather, in these brain regions, adenosine can show persistent and transient signals with a time range of ~5 s [ 96 , 97 ].…”

Toward prediction of abscopal effect in radioimmunotherapy: Pre-clinical investigation

“…Quantitative imaging features (radiomics) were then extracted from the imaging studies [ 8 – 10 , 12 , 13 ]. The radiomics were computed with in-house developed software [ 7 , 14 ]. The MRI radiomics were extracted after intensity normalization [ 15 – 17 ], which is a pre-processing step deemed appropriate for minimizing inter-subject variance due to MRI scanner parameters.…”

“…co-occurrence matrices), and third-order joint probability features, originally described by our group (more details are in the supplementary material) [ 2 ]. The geometric, first-, and second-order features [ 10 ] are among the most commonly used features in the radiomics studies, while the third-order joint probability features have been developed by our group [ 7 ]. Radiomics studies use a wide range of features which they report on—from couple dozens to couple thousands.…”

“…The choice of the quantitative imaging features presented herein was based on their widespread use and their somewhat easer interpretation. All of the radiomics utilized in this study were extracted with in-house developed software [ 7 , 14 , 18 ] interfaced with the Pinnacle (Philips Radiation Oncology Systems, Madison, WI) treatment planning system.…”

“…Immunotherapy (IT) has revolutionized treatment and improved prognosis for patients with various cancers, including lung cancer [ 7 ]. In advanced non-small cell lung cancer (NSCLC), immune-checkpoint inhibitors targeting programmed cell death protein-1 (PD-1) can provide lasting treatment response and improve long-term survival [ 8 , 9 ].…”