Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition

This paper focuses on multi-scale approaches for variational methods and corresponding gradient flows. Recently, for convex regularization functionals such as total variation, new theory and algorithms for nonlinear eigenvalue problems via nonlinear spectral decompositions have been developed. Those methods open new directions for advanced image filtering. However, for an effective use in image segmentation and shape decomposition, a clear interpretation of the spectral response regarding size and intensity scales is needed but lacking in current approaches. In this context, $L^1$ data fidelities are particularly helpful due to their interesting multi-scale properties such as contrast invariance. Hence, the novelty of this work is the combination of $L^1$-based multi-scale methods with nonlinear spectral decompositions. We compare $L^1$ with $L^2$ scale-space methods in view of spectral image representation and decomposition. We show that the contrast invariant multi-scale behavior of $L^1-TV$ promotes sparsity in the spectral response providing more informative decompositions. We provide a numerical method and analyze synthetic and biomedical images at which decomposition leads to improved segmentation.Comment: 13 pages, 7 figures, conference SSVM 201

The homotopy significant spectrum compared to the Krasnoselskii spectrum

How to generalize the concept of eigenvalues of quadratic forms to eigenvalues of arbitrary, even, homogeneous continuous functionals, if stability of the set of eigenvalues under small perturbations is required? We compare two possible generalizations, Gromov's homotopy significant spectrum and the Krasnoselskii spectrum. We show that in the finite dimensional case, the Krasnoselskii spectrum is contained in the homotopy significant spectrum, but provide a counterexample to the opposite inclusion. Moreover, we propose a small modification of the definition of the homotopy significant spectrum for which we can prove stability. Finally, we show that the Cheeger constant of a closed Riemannian manifold corresponds to the second Krasnoselskii eigenvalue

Single tube liquid biopsy for advanced non-small cell lung cancer

The need for a liquid biopsy in non-small cell lung cancer (NSCLC) patients is rapidly increasing. We studied the relation between overall survival (OS) and the presence of four cancer biomarkers from a single blood draw in advanced NSCLC patients: EpCAM(high) circulating tumor cells (CTC), EpCAM(low) CTC, tumor-derived extracellular vesicles (tdEV) and cell-free circulating tumor DNA (ctDNA). EpCAM(high) CTC were detected with CellSearch, tdEV in the CellSearch images and EpCAM(low) CTC with filtration after CellSearch. ctDNA was isolated from plasma and mutations present in the primary tumor were tracked with deep sequencing methods. In 97 patients, 21% had >= 2 EpCAM(high) CTC, 15% had >= 2 EpCAM(low) CTC, 27% had >= 18 tdEV and 19% had ctDNA with >= 10% mutant allele frequency. Either one of these four biomarkers could be detected in 45% of the patients and all biomarkers were present in 2%. In 11 out of 16 patients (69%) mutations were detected in the ctDNA. Two or more unfavorable biomarkers were associated with poor OS. The presence of EpCAM(high) CTC and elevated levels of tdEV and ctDNA was associated with a poor OS; however, the presence of EpCAM(low) CTC was not. This single tube approach enables simultaneous analysis of multiple biomarkers to explore their potential as a liquid biopsy

Enhancing joint reconstruction and segmentation with non-convex Bregman iteration

All imaging modalities such as computed tomography (CT), emission tomography and magnetic resonance imaging (MRI) require a reconstruction approach to produce an image. A common image processing task for applications that utilise those modalities is image segmentation, typically performed posterior to the reconstruction. We explore a new approach that combines reconstruction and segmentation in a unified framework. We derive a variational model that consists of a total variation regularised reconstruction from undersampled measurements and a Chan-Vese based segmentation. We extend the variational regularisation scheme to a Bregman iteration framework to improve the reconstruction and therefore the segmentation. We develop a novel alternating minimisation scheme that solves the non-convex optimisation problem with provable convergence guarantees. Our results for synthetic and real data show that both reconstruction and segmentation are improved compared to the classical sequential approach

Tumor-derived extracellular vesicles for cancer disease management

After a general introduction in Chapter 1, we showed in Chapters 2 and 3, that large tdEVs in blood of metastatic prostate, breast, colorectal and non-small cell lung cancer patients have equivalent prognostic power as CTCs. Importantly, patients with metastatic prostate, breast and colorectal cancer with favorable CTC counts could be further stratified using tdEV counts implying that a subset of patients with favorable CTCs and unfavorable tdEV have a relatively poor outcome and may benefit from a more aggressive treatment similarly to the patients with unfavorable CTC counts. In Chapter 4, we identified large leukocyte-derived extracellular vesicles (ldEVs) in blood of healthy individuals and metastatic cancer patients after immunomagnetic EpCAM enrichment and fluorescent labelling with the CellSearch system. Whereas tdEVs were 20-fold more frequent as compared to CTCs in metastatic cancer patients, the frequency of ldEVs were 5-fold less frequent as compared to leukocytes coming along with the enrichment in both patients and controls. Fluorescence microscopy imaging of whole blood showed the presence of ldEVs in a 3-fold lower frequency as compared to leukocytes suggesting that the “fragmentation” of leukocytes into ldEVs is not caused by processing of blood samples in the CellSearch system and thus, are actually present in blood. In Chapter 5, protocols were developed to image CTCs and tdEVs of castration-resistant prostate cancer patients isolated by the CellSearch system and CTCs isolated using 5 μm filters by scanning electron microscopy (SEM). SEM images of CellSearch enriched CTCs and tdEVs were obtained, but detailed morphologic information was obscured by the presence of ferrofluid, whereas in case of filtration the cells were clearly deformed by the pressure the cells undergo, while entering the filter holes. Interestingly, using SEM many microparticles were observed with similar morphology and size as large tdEVs (EpCAM+, CK+, CD45-, DAPI-) but not detected by fluorescence microscopy. Whether they originate from the tumor or not remains to be further investigated. In Chapter 6, the addition of the HER2 antibody in the CellSearch assay revealed the presence of HER2+, CK-, CD45- CTCs and tdEVs in the blood of breast cancer patients and had a similar association with poor clinical outcome as the CK+, CD45- CTCs. The larger frequency of tdEVs allowed the assessment of the presence of HER2 in a larger portion of patients and encourage the examination of more treatment targets on tdEVs. Importantly, these results pave the path towards a more rational and objective choice of patients who will or will not be subjected to HER2 targeting therapies. In Chapter 7, we compared the presence of CTCs and tdEVs before initiation of therapy and after the first cycle of therapy in CRPC, mBC and mCRC patients to evaluate the effect of therapy on CTCs versus tdEVs. The association between CTCs and tdEVs with overall survival was similar before the initiation of therapy but after the first cycle of therapy, CTCs outperformed tdEVs in mCRC implying that tdEV secretion is dependent on the treatment and possibly the cancer type. The distinction of different tdEV classes using a deep learning approach encourages us to determine the ones that rise or decline after the administration of an effective therapy in order to improve the evaluation of therapy responses and the patient treatment monitoring.In Chapter 8, we investigated whereas we can detect endothelium-derived EVs (edEVs) in the CellSearch image datasets acquired from the CD146 enriched blood samples of metastatic colorectal cancer (mCRC) patients. Circulating endothelial cells (CECs) are significantly elevated in the blood circulation of cancer patients compared to healthy individuals; their presence is however not associated with better or poorer clinical outcome. The CEC number is biased by venipuncture procedure as endothelial cells are released due to the vacuum and enter the collection tube. edEVs should not be influenced; so we explored their presence through ACCEPT analysis and revealed that edEVs are detected at 5- to 10- fold higher frequencies compared to CECs. Moreover, their counts correlated with the clinical outcome of the patients. Importantly, the final multivariate Cox regression model included both tdEVs and edEVs as significant independent prognostic markers of the overall survival of mCRC patients. The elevated edEV counts denote either their active role in promoting tumor angiogenesis or/and their passive secretion because of the growth of the tumor. If the former hypothesis is correct, then edEVs could serve as a promising biomarker to predict patients that could benefit from anti-angiogenic treatments. Whether edEVs could serve as a more informative diagnostic tool in cardiovascular diseases remains to be investigated.In Chapter 9, three different (des)biotin liposomes were compared in terms of their fusion with cells from cancer cell lines and blood from healthy individuals. DOPC liposomes of similar size distribution containing 20 mol% DOPE-desbiotin, 20 mol% DOPE-biotin or 5 mol% chol-EG3-biotin were prepared by extrusion. Their mean hydrodynamic diameter was around 100 nm. Leukocyte subpopulations, platelets and different cancer cell lines were incubated with the different liposome systems, stained with fluorophore-tagged streptavidin and their fusion with liposomes was assessed by flow cytometry and immunofluorescence microscopy. The chol-EG3-biotin liposomes achieved the highest biotin incorporation into the cell membrane for all different cell types, followed by DOPE-biotin liposomes. DOPE-desbiotin liposomes did not show considerable fusion with any cell type. The highest liposome uptake was found in cancer cells followed by the monocytes indicating a relationship between available cell surface and liposome uptake. <br/

Classification of Cells in CTC-Enriched Samples by Advanced Image Analysis

In the CellSearch® system, blood is immunomagnetically enriched for epithelial cell adhesion molecule (EpCAM) expression and cells are stained with the nucleic acid dye 4′6-diamidino-2-phenylindole (DAPI), Cytokeratin-PE (CK), and CD45-APC. Only DAPI+/CK+ objects are presented to the operator to identify circulating tumor cells (CTC) and the identity of all other cells and potential undetected CTC remains unrevealed. Here, we used the open source imaging program Automatic CTC Classification, Enumeration and PhenoTyping (ACCEPT) to analyze all DAPI+ nuclei in EpCAM-enriched blood samples obtained from 192 metastatic non-small cell lung cancer (NSCLC) patients and 162 controls. Significantly larger numbers of nuclei were detected in 300 patient samples with an average and standard deviation of 73,570 ± 74,948, as compared to 359 control samples with an average and standard deviation of 4191 ± 4463 (p < 0.001). In patients, only 18% ± 21% and in controls 23% ± 15% of the nuclei were identified as leukocytes or CTC. Adding CD16-PerCP for granulocyte staining, the use of an LED as the light source for CD45-APC excitation and plasma membrane staining obtained with wheat germ agglutinin significantly improved the classification of EpCAM-enriched cells, resulting in the identification of 94% ± 5% of the cells. However, especially in patients, the origin of the unidentified cells remains unknown. Further studies are needed to determine if undetected EpCAM+/DAPI+/CK-/CD45- CTC is present among these cells