Results: Results on phantom and clinical images were compared with a manual localization giving mean errors of 1.09 ± 0.61 mm on phantom image and 1.44 ± 0.45 mm on clinical images. On clinical images, the mean errors of individual seeds orientation was 4.33 ± 8 . 51 ∘ .
Methods: The presented method is based on a prelocalization of the needles through which the seeds are injected in the prostate. This prelocalization allows focusing the search on a region of interest (ROI) around the needle tip. Seeds localization starts by binarizing the ROI and removing false positives using, respectively, a Bayesian classifier and a support vector machine (SVM). This is followed by a registration stage using first an iterative closest point (ICP) for localizing the connected set of seeds (named strand) inserted through a needle, and secondly refining each seed position using sum of squared differences (SSD) as a similarity criterion. ICP registers a geometric model of the strand to the candidate voxels while SSD compares an appearance model of a single seed to a subset of the image. The method was evaluated both for 3D images of an Agar-agar phantom and a dataset of clinical 3D images. It was tested on stranded and on loose seeds.
Purpose: New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position and orientation of the implanted radioactive seeds from real-time ultrasound (US) images. This is necessary to compare them to the planned one and to adjust automatically the dosimetric plan accordingly for next seeds implantations. The image modality, the small size of the seeds, and the artifacts they produce make it a very challenging problem. The objective of the presented work is to setup and to evaluate a robust and automatic method for seed localization in three-dimensional (3D) US images.
Conclusions: The proposed algorithm for radioactive seed localization is robust, tested on different US images, accurate, giving small mean error values, and returns the five cylindrical seeds degrees of freedom.
Keywords: 3D Ultrasound image; Bayesian classifier; iterative closest point (ICP); prostate brachytherapy; radioactive seed localization; sum of squared differences (SSD); support vector machine (SVM).
This means that patients from all walks of life can enjoy the medicinal properties of CBD 3D marijuana seeds. With 13% CBD, it’s an excellent herbal alternative for pain, and many find that it’s clear-headed high can help stimulate the creative mind.
You won’t need a special set of frames to enjoy CBD 3D marijuana, just somewhere to go so you won’t waste your new, cheerful mood!
Often, when a strain contains CBD in the title, recreational users tend to steer clear, but CBD 3D marijuana will put a smile on the face of any person who tries it, even if you’re not testing it out for therapeutic purposes. The THC count is low, usually between 6% and 8%, but its enough to bring a little sunshine to a cloud day. In fact, CBD 3D marijuana is considered primarily a daytime strain because of the energy, and beginners to dosing with marijuana have found it to be a great way to ease themselves into it, since the CBD helps to alleviate any anxiety or paranoia that might occur with THC.
A perfect blend of CBD and THC, CBD 3D marijuana seeds produce plants that pop and effects that will leave you happily wanting more.
CBD 3D itself doesn’t have the most appealing aroma, unless you enjoy the scent of wet earth. It is accompanied by notes of pine and mango, which are more noticeable after drying and curing.
Because this strain is dominant in sativa, it grows tall when given the warm, Mediterranean climates it prefers. You could grow CBD 3D marijuana outdoors, but you’d be better off setting them up in a soilless environment inside where you can monitor every stage of growth. After 10-12 weeks of flowering, you’ll be able to enjoy upwards of 16 ounces of sweet medical herb per plant.