Motion magnification analysis of microscopy videos of biological cells

Fiduciary-Free Frame Alignment for Robust Time-Lapse Drift Correction Estimation in Multi-Sample Cell Microscopy

When analyzing microscopic time-lapse observations, frame alignment is an essential task to visually understand the morphological and translation dynamics of cells and tissue. While in traditional single-sample microscopy, the region of interest (RoI) is fixed, multi-sample microscopy often uses a single microscope that scans multiple samples over a long period of time by laterally relocating the sample stage. Hence, the relocation of the optics induces a statistical RoI offset and can introduce jitter as well as drift, which results in a misaligned RoI for each sample's time-lapse observation (stage drift). We introduce a robust approach to automatically align all frames within a time-lapse observation and compensate for frame drift. In this study, we present a sub-pixel precise alignment approach based on recurrent all-pairs field transforms (RAFT); a deep network architecture for optical flow. We show that the RAFT model pre-trained on the Sintel dataset performed with near perfect precision for registration tasks on a set of ten contextually unrelated time-lapse observations containing 250 frames each. Our approach is robust for elastically undistorted and translation displaced (x,y) microscopic time-lapse observations and was tested on multiple samples with varying cell density, obtained using different devices. The approach only performed well for registration and not for tracking of the individual image components like cells and contaminants. We provide an open-source command-line application that corrects for stage drift and jitter.

Applying video motion magnification to reveal spontaneous tympanic membrane displacement as an indirect measure of intracranial pressure in patients with brain pathologies

BACKGROUND

The observation of tympanic membrane displacement (TMD) opens up the possibility of indirect intracranial pressure (ICP) estimation. In this study, we applied a phase-based video motion magnification (VMM) algorithm to reveal spontaneous pulse TMD waveforms (spTMD) and compare them with invasively measured ICP in patients with intracranial pathologies.

METHODS

Nine adults (six traumatic brain injury and three aneurysmal subarachnoid haemorrhage; median age 44 (29-53) years admitted to the intensive care unit of Wroclaw Medical University between October 2021 and October 2022 with implanted ICP sensors were included in this retrospective study. Video recordings of the tympanic membrane were performed using a portable otoscope with a video camera and analysed by a custom-written VMM algorithm. ICP was monitored using intraparenchymal sensors and arterial blood pressure (ABP) was measured in the radial arterial lines. ICP, ABP, and spTMD videos were captured simultaneously. The pulse amplitudes of ICP (Amp_ICP), ABP (Amp_ABP) and spTMD (Amp_spTMD) were estimated using fast Fourier transform within the heart rate (HR)-related frequency range.

RESULTS

Amp_spTMD was significantly correlated with mean ICP (rS = 0.73; p = 0.025) and with Amp_ICP (rS = 0.88; p = 0.002). Age was not a significant moderator of this association. There were no significant relationships between Amp_spTMD and either mean ABP, HR, or Amp_ABP.

CONCLUSIONS

The study suggests that Amp_spTMD increases with the increase in mean ICP and Amp_ICP. Estimation of Amp_spTMD using the VMM algorithm has the potential to allow for non-invasive detection of the risk of elevated ICP; however, further investigation in a larger group of patients is required.