Frequency analysis for an extended photoacoustic transport model.

In photoacoustic imaging, the signal attenuation is a well-known source of artifacts over the image reconstruction. It is recognized that this is caused by optical absorption effects and by the ultrasound broadband scattering. However, the sound dispersion is generally neglected, although it appears notably in thick or heterogeneous tissues. In the present Letter, we give an experimental example in which both attenuation and sound dispersion are dealt with as relevant features to be taken into consideration. An analytic perspective of these perturbations leads us to a waveform transport-model extension that provides a linear description of the induced acoustic effects. We find a near match between the theoretical predictions and the experimental results in the frequency domain. These outcomes approximate projection data that represent forward solutions in photoacoustic image reconstruction.

Automatic Segmentation of the Cerebellum in Ultrasound Volumes of the Fetal Brain / Segmentación automática del cerebelo en volúmenes de ultrasonido del cerebro fetal

The size of the cerebellum in ultrasound volumes of the fetal brain has shown a high correlation with gestational age, which makes it a valuable feature to detect fetal growth restrictions. Manual annotation of the 3D surface of the cerebellum in an ultrasound volume is a time consuming task, which needs to be performed by a highly trained expert. In order to assist the experts in the evaluation of cerebellar dimensions, we developed an automatic scheme for the segmentation of the 3D surface of the cerebellum in ultrasound volumes, using a spherical harmonics model. In this work we present our validation results on 10 ultrasound volumes in which we have obtained an adequate accuracy in the segmentation of the cerebellum (mean Dice coefficient of 0.689). The method reported shows potential to effectively assist the experts in the assessment of fetal growth in ultrasound volumes.

El tamaño del cerebelo, en un volumen de ultrasonido del cerebro fetal, ha mostrado una alta correlación con la edad gestacional, lo que hace importante a esta medición para la detección de restricciones del crecimiento del feto. La anotación manual de la superficie 3D del cerebelo en un volumen de ultrasonido es una tarea demandante, que debe ser realizada por un experto. Con el propósito de apoyar a los expertos en la evaluación de las dimensiones del cerebelo fetal, hemos desarrollado un método automático para la segmentación de la superficie 3D del cerebelo en volúmenes de ultrasonido, utilizando un modelo de harmónicos esféricos (spherical harmonics). En este trabajo presentamos los resultados de una evaluación del método automático en 10 volúmenes de ultrasonido con los que hemos obtenido un valor adecuado de exactitud (coeficiente promedio de Dice de 0.689). El método reportado tiene potencial para asistir de manera efectiva a los expertos en la evaluación del crecimiento fetal, utilizando volúmenes de ultrasonido.

Automatic analysis of immunocytochemically stained tissue samples.

An automatic colour image segmentation and cell counting software system has been developed for immunocytochemical analysis of stained tissue samples. The system was designed to count the total number of positive and negative cells in tissue samples treated with cytokine DNA probes from pigs naturally parasitised with Taenia solium metacestodes, using in situ hybridisation. A reaction index was calculated as the ratio of the number of cells with a positive reaction to the total number of cells (positives plus negatives) for each of five different probes. The objectives of automatic counting were to improve the reproducibility of the analysis and reduce the processing time of large image batches. A fast KNN classifier was used for colour segmentation. Watershed segmentation combined with edge detection was used to isolate individual cells that were then automatically labelled, using the results of the corresponding colour segmented image. Validation was performed on 122 non-training digital images with a total of 1069 positive cells and 1459 negative cells, with the following results: a mean true positive rate of 90.2% for positive cells and a mean true positive rate of 85.4% for negative cells. The corresponding mean false positive rates were 9.6% and 6.6%. The mean reaction index error of the automatic analysis was 5.35%. The processing of each digital image took 10 s on a Pentium IV PC.

Automatic identification of metaphase spreads and nuclei using neural networks.

The mitotic index (MI) is an important measure in cell proliferation studies. Determination of the MI is usually made by light-microscope analysis of slide preparations. The analyst identifies and counts thousands of cells and reports the percentage of mitotic shapes found among the interphase nuclei. Full automation of this process is an ambitious task, because there can exist very few mitotic shapes among hundreds of nuclei and thousands of artifacts, resulting in a high probability of false positives, i.e. objects erroneously identified as mitosis or nuclei. A semi-automated approach for MI calculation is reported, based on the development of a neural network (NN) for automatic identification of metaphase spreads and stimulated nuclei in digital images of microscope preparations at 10X magnification. After segmentation of the objects on each image, ten different morphometrical, photometrical and textural features are measured on each segmented object. An NN is used to classify the feature vectors into three classes: metaphases, nuclei and artifacts. The system has been able to classify correctly approximately 91% of the objects in each class, in a test set of 191 mitosis, 331 nuclei and 387 artifacts, obtained from 30 different microscope slides. Manual editing of false positives from the metaphase classification results allows the calculation of the MI with an error of 6.5%.

Automated prostate recognition: a key process for clinically effective robotic prostatectomy.

Clinical trials of PROBOT, a robotic system for prostate surgery, have shown that robotic surgery of soft tissue can be successful. Monitoring of the progress of the resection has shown to be a necessary feature of an effective robotic system for prostate surgery. It should provide the surgeon with a reliable method of assessing the cavity during resection. An automatic system for intraoperative monitoring of the progress of the resection during robotic prostatectomy consists of two subsystems: real-time intraoperative imaging of the prostate and automatic identification of the contour of the gland on each image. The development of a fully automatic scheme for prostate recognition on transurethral ultrasound scans is reported. A genetic algorithm has been developed to automatically adjust a model of the prostate boundary until an optimum fit to the prostate in a given image is obtained. An analysis of its performance on 22 different ultrasound images showed an average error of 6.21 mm. Use of a genetic algorithm and a constrained prostate model have shown to be a robust way to automatically identify the prostate in ultrasound images. The scheme is able to produce approximate prostate boundaries, without any human intervention, on ultrasound scans of varying quality. In addition to soft tissue robotic surgery, the genetic algorithm technique is also applicable to a wide range of computer assisted surgical techniques.

Electromagnetic compatibility aspects of active robotic systems for surgery: the robotic prostatectomy experience.

The paper discusses the susceptibility to electromagnetic interference (EMI) of active robots for surgery, which are safety-critical systems. The high EMI environment of an operating room in the presence of an electrosurgical generator is considered. Experience of a surgeon assistant robot for prostatectomies in improving the immunity to EMI is described. It has been found that effective isolation of the robotic system hardware from grounded metal objects provides significant improvements to safety by its immunity to EMI, in minimising the flow of high-frequency current to ground through the system hardware.

The Probot--an active robot for prostate resection.

As men age, their prostates can enlarge, causing urinary difficulty. Surgery to correct this [transurethral resection of the prostate (TURP)] is a skilled and time-consuming operation requiring many repetitive motions of a cutter. A robot has been developed to perform these motions, relieving the surgeon of much of the burden of surgery. This robot has been tried both in the laboratory and later on human subjects and has proved itself capable of performing prostate resection. The Probot system consists of on-line imaging and three-dimensional prostate model construction, an appropriate surgeon-computer interface, a counterbalanced mounting frame and a computer controlled robot.