Search Results (4)

Purpose: To develop and validate a deep-learning-based algorithm (DLA) that is designed to segment and classify metallic objects in topograms of abdominal and spinal CT. Methods: DLA training for implant segmentation and classification was based on a U-net-like architecture with 263 annotated hip implant topograms and 2127 annotated spine implant topograms. The trained DLA was validated with internal and external datasets. Two radiologists independently reviewed the external dataset consisting of 2178 abdomen anteroposterior (AP) topograms and 515 spine AP and lateral topograms, all collected in a consecutive manner. Sensitivity and specificity were calculated per pixel row and per patient. Pairwise intersection over union (IoU) was also calculated between the DLA and the two radiologists. Results: The performance parameters of the DLA were consistently >95% in internal validation per pixel row and per patient. DLA can save 27.4% of reconstruction time on average in patients with metallic implants compared to the existing iMAR. The sensitivity and specificity of the DLA during external validation were greater than 90% for the detection of spine implants on three different topograms and for the detection of hip implants on abdominal AP and spinal AP topograms. The IoU was greater than 0.9 between the DLA and the radiologists. However, the DLA training could not be performed for hip implants on spine lateral topograms. Conclusions: A prototype DLA to detect metallic implants of the spine and hip on abdominal and spinal CT topograms improves the scan workflow with good performance for both spine and hip implants. Full article

Background: We investigated the feasibility of a deep learning algorithm (DLA) based on apparent diffusion coefficient (ADC) maps for the segmentation and discrimination of clinically significant cancer (CSC, Gleason score ≥ 7) from non-CSC in patients with prostate cancer (PCa). Methods: Data from a total of 149 consecutive patients who had undergone 3T-MRI and been pathologically diagnosed with PCa were initially collected. The labelled data (148 images for GS6, 580 images for GS7) were applied for tumor segmentation using a convolutional neural network (CNN). For classification, 93 images for GS6 and 372 images for GS7 were used. For external validation, 22 consecutive patients from five different institutions (25 images for GS6, 70 images for GS7) representing different MR machines were recruited. Results: Regarding segmentation and classification, U-Net and DenseNet were used, respectively. The tumor Dice scores for internal and external validation were 0.822 and 0.7776, respectively. As for classification, the accuracies of internal and external validation were 73 and 75%, respectively. For external validation, diagnostic predictive values for CSC (sensitivity, specificity, positive predictive value and negative predictive value) were 84, 48, 82 and 52%, respectively. Conclusions: Tumor segmentation and discrimination of CSC from non-CSC is feasible using a DLA developed based on ADC maps (b2000) alone. Full article

Internal diffusion limited aggregation (IDLA) is a random aggregation model on a graph G, whose clusters are formed by random walks started in the origin (some fixed vertex) and stopped upon visiting a previously unvisited site. On the Sierpinski gasket graph, the asymptotic shape is known to be a ball in the graph metric. In this paper, we improve the sublinear bounds for the fluctuations known from its known asymptotic shape result by establishing bounds for the odometer function for a divisible sandpile model. Full article

Cardiac surgical approaches require the development of new materials regardless of the polyurethanes used for pulsatile blood pumps; therefore, an innovative biomaterial, a copolymer of poly(ethylene terephthalate) and dimer fatty acid (dilinoleic acid) modified with D-glucitol, hereafter referred to as PET/DLA, has been developed, showing non-hemolytic and atrombogenic properties and resistance to biodegradation. The aim of this work was to evaluate in vivo inflammatory responses to intramuscular implantation of PET/DLA biomaterials of different compositions (hard to soft segments). Two copolymers containing 70 and 65 wt.% of hard segments, as in poly(ethylene terephthalate) and dilinoleic acid in soft segments modified with D-glucitol, were used for implantation tests to monitor tissue response. Medical grade polyurethanes Bionate II 90A and Bionate II 55 were used as reference materials. After euthanasia of animals (New Zealand White rabbits, n = 49), internal organs and tissues that contacted the material were collected for histopathological examination. The following parameters were determined: peripheral blood count, blood smear with May Grunwald–Giemsa staining, and serum C-reactive protein (CRPP). The healing process observed at the implantation site of the new materials after 12 weeks indicated normal progressive collagenization of the scar, with an indication of the inflammatory–resorptive process. The analysis of the chemical structure of explants 12 weeks after implantation showed good stability of the tested copolymers in contact with living tissues. Overall, the obtained results indicate great potential for PET/DLA in medical applications; however, final verification of its applicability as a structural material in prostheses is needed. Full article