54 research outputs found
Multispectral image analysis in laparoscopy â A machine learning approach to live perfusion monitoring
Modern visceral surgery is often performed through small incisions. Compared to open surgery, these minimally invasive interventions result in smaller scars, fewer complications and a quicker recovery. While to the patients benefit, it has the drawback of limiting the physicianâs perception largely to that of visual feedback through a camera mounted on a rod lens: the laparoscope. Conventional laparoscopes are limited by âimitatingâ the human eye. Multispectral cameras remove this arbitrary restriction of recording only red, green and blue colors. Instead, they capture many specific bands of light. Although these could help characterize important indications such as ischemia and early stage adenoma, the lack of powerful digital image processing prevents realizing the techniqueâs full potential.
The primary objective of this thesis was to pioneer fluent functional multispectral imaging (MSI) in laparoscopy. The main technical obstacles were: (1) The lack of image analysis concepts that provide both high accuracy and speed. (2) Multispectral image recording is slow, typically ranging from seconds to minutes. (3) Obtaining a quantitative ground truth for the measurements is hard or even impossible.
To overcome these hurdles and enable functional laparoscopy, for the first time in this field physical models are combined with powerful machine learning techniques. The physical model is employed to create highly accurate simulations, which in turn teach the algorithm to rapidly relate multispectral pixels to underlying functional changes. To reduce the domain shift introduced by learning from simulations, a novel transfer learning approach automatically adapts generic simulations to match almost arbitrary
recordings of visceral tissue. In combination with the only available video-rate capable multispectral sensor, the method pioneers fluent perfusion monitoring with MSI. This system was carefully tested in a multistage process, involving in silico quantitative evaluations, tissue phantoms and a porcine study. Clinical applicability was ensured through in-patient recordings in the context of partial nephrectomy; in these, the novel system characterized ischemia live during the intervention. Verified against a fluorescence reference, the results indicate that fluent, non-invasive ischemia detection and monitoring is now possible.
In conclusion, this thesis presents the first multispectral laparoscope capable of videorate functional analysis. The system was successfully evaluated in in-patient trials, and future work should be directed towards evaluation of the system in a larger study. Due to the broad applicability and the large potential clinical benefit of the presented functional estimation approach, I am confident the descendants of this system are an integral part
of the next generation OR
Die hydrophobe fest-flĂŒssig-GrenzflĂ€che unter hohem hydrostatischen Druck
Im Rahmen dieser Arbeit wurde die GrenzflĂ€che zwischen hydrophoben OberflĂ€chen und Wasser experimentell untersucht. Es wurde das Verhalten der an der GrenzflĂ€che auftretenden Schicht geringer Dichte, des sogenannten hydrophoben Gaps, unter hohen hydrostatischen DrĂŒcken bis zu 5 kbar studiert. Als Modellsystem diente ein mit einer molekularen Monolage Octadecyltrichlorosilan (OTS) beschichteter Siliziumwafer in Kontakt mit Wasser. Die Untersuchungen wurden mittels Röntgen-Reflektometrie (XRR) durchgefĂŒhrt. HierfĂŒr wurde die erste Hochdruck-XRR-Probenzelle angefertigt, die bis zu einem Druck von 5 kbar arbeitet. Die erhaltenen Ergebnisse zeigen, dass das hydrophobe Gap bis zu einem Druck von ca. 1,5 kbar stĂ€rker komprimiert wird als das umgebende Volumenwasser, wĂ€hrend das hydrophobe Gap bei höheren DrĂŒcken weniger stark komprimiert wird. Das deutet darauf hin, dass bei Umgebungsdruck eine im Vergleich zu Volumenwasser offenere Netzwerkstruktur des Wassers an der GrenzflĂ€che vorliegt, die bei hohem hydrostatischen Druck zumindest teilweise geschlossen wird. Weiterhin wurde der Einfluss kosmotroper und chaotroper Kosolvenzien auf die Struktur der hydrophoben fest-flĂŒssig-GrenzflĂ€che untersucht. Als Kosmotrop wurde Trimethylamin-N-Oxid (TMAO) und als Chaotrop Urea verwendet. Beide Stoffe adsorbierten an der GrenzflĂ€che und erzeugten so eine lokal erhöhte Konzentration im Vergleich zur VolumenflĂŒssigkeit. Die KompressibilitĂ€t der adsorbierten Schichten ist sowohl stoff- als auch konzentrationsabhĂ€ngig, jedoch immer geringer als bei Volumenwasser
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera proffers a technical challenge. Herein, we propose a new approach to
anatomical structure classification and image tagging that features an
intrinsic measure of confidence to estimate its own performance with high
reliability and which can be applied to both RGB and multispectral imaging (MI)
data. Methods: Organ recognition is performed using a superpixel classification
strategy based on textural and reflectance information. Classification
confidence is estimated by analyzing the dispersion of class probabilities.
Assessment of the proposed technology is performed through a comprehensive in
vivo study with seven pigs. Results: When applied to image tagging, mean
accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
and 96% (MI) with the confidence measure. Conclusion: Results showed that the
confidence measure had a significant influence on the classification accuracy,
and MI data are better suited for anatomical structure labeling than RGB data.
Significance: This work significantly enhances the state of art in automatic
labeling of endoscopic videos by introducing the use of the confidence metric,
and by being the first study to use MI data for in vivo laparoscopic tissue
classification. The data of our experiments will be released as the first in
vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table
Multispectral imaging of organ viability during uterine transplantation surgery in rabbits and sheep
Uterine transplantation surgery (UTx) has been proposed as a treatment for permanent absolute uterine factor infertility (AUFI) in the case of the congenital absence or surgical removal of the uterus. Successful surgical attachment of the organ and its associated vasculature is essential for the organâs reperfusion and long-term viability. Spectral imaging techniques have demonstrated the potential for the measurement of hemodynamics in medical applications. These involve the measurement of reflectance spectra by acquiring images of the tissue in different wavebands. Measures of tissue constituents at each pixel can then be extracted from these spectra through modeling of the lightâtissue interaction. A multispectral imaging (MSI) laparoscope was used in sheep and rabbit UTx models to study short- and long-term changes in oxygen saturation following surgery. The whole organ was imaged in the donor and recipient animals in parallel with point measurements from a pulse oximeter. Imaging results confirmed the re-establishment of adequate perfusion in the transplanted organ after surgery. Cornual oxygenation trends measured with MSI are consistent with pulse oximeter readings, showing decreased StO2 immediately after anastomosis of the blood vessels. Long-term results show recovery of StO2 to preoperative levels
Tissue classification for laparoscopic image understanding based on multispectral texture analysis.
Intraoperative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study through statistical analysis, we show that (1)Â multispectral imaging data are superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2)Â combining the tissue texture with the reflectance spectrum improves the classification performance. The classifier reaches an accuracy of 98.4% on our dataset. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy
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