Automatic segmentation of deep intracerebral electrodes in computed tomography scans

Background: Invasive monitoring of brain activity by means of intracerebral electrodes is widely practiced to improve pre-surgical seizure onset zone localization in patients with medically refractory seizures. Stereo-Electroencephalography (SEEG) is mainly used to localize the epileptogenic zone and a precise knowledge of the location of the electrodes is expected to facilitate the recordings interpretation and the planning of resective surgery. However, the localization of intracerebral electrodes on post-implant acquisitions is usually time-consuming (i.e., manual segmentation), it requires advanced 3D visualization tools, and it needs the supervision of trained medical doctors in order to minimize the errors. In this paper we propose an automated segmentation algorithm specifically designed to segment SEEG contacts from a thresholded post-implant Cone-Beam CT volume (0.4 mm, 0.4 mm, 0.8 mm). The algorithm relies on the planned position of target and entry points for each electrode as a first estimation of electrode axis. We implemented the proposed algorithm into DEETO, an open source C++ prototype based on ITK library. Results: We tested our implementation on a cohort of 28 subjects in total. The experimental analysis, carried out over a subset of 12 subjects (35 multilead electrodes; 200 contacts) manually segmented by experts, show that the algorithm: (i) is faster than manual segmentation (i.e., less than 1s/subject versus a few hours) (ii) is reliable, with an error of 0.5 mm +/- 0.06 mm, and (iii) it accurately maps SEEG implants to their anatomical regions improving the interpretability of electrophysiological traces for both clinical and research studies. Moreover, using the 28-subject cohort we show here that the algorithm is also robust (error <0.005 mm) against deep-brain displacements (<12 mm) of the implanted electrode shaft from those planned before surgery. Conclusions: Our method represents, to the best of our knowledge, the first automatic algorithm for the segmentation of SEEG electrodes. The method can be used to accurately identify the neuroanatomical loci of SEEG electrode contacts by a non-expert in a fast and reliable manner.Peer reviewe

Mechanically-tuned alginate gels as new 3D breast cancer models

Three-dimensional (3D) cell cultures represent fundamental tools for the comprehension of cellular phenomena both in normal and pathological conditions. In particular, mechanical stimuli not less than chemical ones have a relevant role on cell fate, cancer onset and malignant progression. Here, we realize mechanically tuned alginate hydrogels for studying the role of substrate elasticity on breast adenocarcinoma cells activity. Hydrogels Elastic Modulus (E) was measured via Atomic Force Microscopy and a remarkable range (20\u20134000 kPa) was obtained. A breast cancer cell line, MCF-7, was seeded within the 3D gels, on standard Petri and alginate-coated dishes (2D controls). Cells showed dramatic morphological differences when cultured in 3D vs. 2D, exhibiting a flat shape morphology in both 2D conditions, while they maintained within gels a circular, clusterorganized conformation similar to the in vivo one. In 3D culture, we observed a strict correlation between cells viability and substrate elasticity; in particular, MCF-7s constantly decreased in number with increasing hydrogel elasticity. The highest cellular proliferation rate, associated to a formation of cell clusters, occurred in two weeks of culture only within the softest hydrogels (E=20-40 kPa), highlighting the need of adopting more realistic and a priori defined models for in vitro cancer studies

3D Bioprinting as a Powerful Technique for Recreating the Tumor Microenvironment

In vitro three-dimensional models aim to reduce and replace animal testing and establish new tools for oncology research and the development and testing of new anticancer therapies. Among the various techniques to produce more complex and realistic cancer models is bioprinting, which allows the realization of spatially controlled hydrogel-based scaffolds, easily incorporating different types of cells in order to recreate the crosstalk between cancer and stromal components. Bioprinting exhibits other advantages, such as the production of large constructs, the repeatability and high resolution of the process, as well as the possibility of vascularization of the models through different approaches. Moreover, bioprinting allows the incorporation of multiple biomaterials and the creation of gradient structures to mimic the heterogeneity of the tumor microenvironment. The aim of this review is to report the main strategies and biomaterials used in cancer bioprinting. Moreover, the review discusses several bioprinted models of the most diffused and/or malignant tumors, highlighting the importance of this technique in establishing reliable biomimetic tissues aimed at improving disease biology understanding and high-throughput drug screening