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I escaped the windy and rainy Netherlands to the sunny Mediterranean island of Malta, where I headed to Divewise to meet up with John Kendall, a GUE instructor with a passion for photogrammetry. Photogrammetry is the practice of gathering information about objects using photographs and 3D modeling. I spent the next five days practicing these skills with John, whose specialty is shipwreck photogrammetry. We started out in the classroom by running through the basics of Agisoft, the software used to create the 3D models. John taught me that the key to making models was 80% overlap between successive photos, which would allow the software to match up the images.

It is also important that the successive photos do not change angle too quickly or round corners too fast. We made models of various items around the Divewise shop before heading underwater at the house reef. My first model! A rocky basin on the house reef. What was relatively simple on land took on a different level of difficulty underwater. Lots of practice led to a simple model of the seabed. I spent the next few days practicing on the famous Maltese wrecks under John’s watchful eye, ultimately creating this model of the bow of the P29 wreck.

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Model of the bow of the P29 wreck. Learning photogrammetry skills was incredibly fun and challenging. Thank you to John for hosting me, and thank you to and for providing the camera setup that I used on this experience! Thank you also to and, as well as Rolex and OWUSS for this opportunity.

Scroll down to see some more photos from MaltaI’m off to China next! John hovers above the wreck of the P29.

He’s modeling the whole ship while I work on making a model of the bow! Photo by Leah Potts A doorway on the wreck of the Um El Faroud. Photo by Leah Potts. Wreck of the Rozi. Photo by Leah Potts. Swimming on the Um El Faroud. Photo by Leah Potts.

Abstract Introduction. Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input. Materials and methods.

We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data. Results show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm. The proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation- based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field.