332,508 research outputs found
Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting
For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.publishedVersio
Preoperative Brain Tumor Imaging:Models and Software for Segmentation and Standardized Reporting
For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports
A Framework for Efficient Adaptively Secure Composable Oblivious Transfer in the ROM
Oblivious Transfer (OT) is a fundamental cryptographic protocol that finds a
number of applications, in particular, as an essential building block for
two-party and multi-party computation. We construct a round-optimal (2 rounds)
universally composable (UC) protocol for oblivious transfer secure against
active adaptive adversaries from any OW-CPA secure public-key encryption scheme
with certain properties in the random oracle model (ROM). In terms of
computation, our protocol only requires the generation of a public/secret-key
pair, two encryption operations and one decryption operation, apart from a few
calls to the random oracle. In~terms of communication, our protocol only
requires the transfer of one public-key, two ciphertexts, and three binary
strings of roughly the same size as the message. Next, we show how to
instantiate our construction under the low noise LPN, McEliece, QC-MDPC, LWE,
and CDH assumptions. Our instantiations based on the low noise LPN, McEliece,
and QC-MDPC assumptions are the first UC-secure OT protocols based on coding
assumptions to achieve: 1) adaptive security, 2) optimal round complexity, 3)
low communication and computational complexities. Previous results in this
setting only achieved static security and used costly cut-and-choose
techniques.Our instantiation based on CDH achieves adaptive security at the
small cost of communicating only two more group elements as compared to the
gap-DH based Simplest OT protocol of Chou and Orlandi (Latincrypt 15), which
only achieves static security in the ROM
Climbing Mount Scalable: Physical-Resource Requirements for a Scalable Quantum Computer
The primary resource for quantum computation is Hilbert-space dimension.
Whereas Hilbert space itself is an abstract construction, the number of
dimensions available to a system is a physical quantity that requires physical
resources. Avoiding a demand for an exponential amount of these resources
places a fundamental constraint on the systems that are suitable for scalable
quantum computation. To be scalable, the effective number of degrees of freedom
in the computer must grow nearly linearly with the number of qubits in an
equivalent qubit-based quantum computer.Comment: LATEX, 24 pages, 1 color .eps figure. This new version has been
accepted for publication in Foundations of Physic
Synthesis of Topological Quantum Circuits
Topological quantum computing has recently proven itself to be a very
powerful model when considering large- scale, fully error corrected quantum
architectures. In addition to its robust nature under hardware errors, it is a
software driven method of error corrected computation, with the hardware
responsible for only creating a generic quantum resource (the topological
lattice). Computation in this scheme is achieved by the geometric manipulation
of holes (defects) within the lattice. Interactions between logical qubits
(quantum gate operations) are implemented by using particular arrangements of
the defects, such as braids and junctions. We demonstrate that junction-based
topological quantum gates allow highly regular and structured implementation of
large CNOT (controlled-not) gate networks, which ultimately form the basis of
the error corrected primitives that must be used for an error corrected
algorithm. We present a number of heuristics to optimise the area of the
resulting structures and therefore the number of the required hardware
resources.Comment: 7 Pages, 10 Figures, 1 Tabl
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