PET-CT images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in PET or low contrast in CT. In this work we propose a novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. The approach formulates the segmentation problem as a minimization problem of a Markov Random Field (MRF) model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the PET and the CT images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two subgraphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 PET-CT datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using PET or CT.

In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data.

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PurposeTo quantify the dosimetric impact of applicator displacements and applicator reconstruction-uncertainties through simulated planning studies of virtual applicator shifts.Material and methodsTwenty randomly selected high-dose-rate (HDR) titanium tandem-and-ovoid (T&O) plans were retrospectively studied. MRI-guided, conformal brachytherapy (MRIG-CBT) plans were retrospectively generated. To simulate T&O displacement, the whole T&O set was virtually shifted on treatment planning system in the cranial (+) and the caudal (–) direction after each dose calculation. Each shifted plan was compared to an unshifted plan. To simulate T&O reconstruction-uncertainties, each tandem and ovoid was separately shifted along its axis before performing the dose calculation. After the dose calculation, the calculated isodose lines and T&O were moved back to unshifted T&O position. Shifted and shifted-back plan were compared.ResultsRegarding the dosimetric impact of the simulated T&O displacements, rectal D2cc values were observed as being the most sensitive to change due to T&O displacement among all dosimetric metrics regardless of point A (p < 0.013) or MRIG-CBT plans (p < 0.0277). To avoid more than 10% change, ± 1.5 mm T&O displacements were accommodated for both point A and MRIG-CBT plans. The dosimetric impact of T&O displacements on sigmoid (p < 0.0005), bladder (p < 0.0001), HR-CTV (p < 0.0036), and point A (p < 0.0015) were significantly larger in the MRIG-CBT plans than point A plans. Regarding the dosimetric impact of T&O reconstruction-uncertainties, less than ± 3.0 mm reconstruction-uncertainties were also required in order to avoid more than 10% dosimetric change in either the point A or MRIG-CBT plans.ConclusionsThe dosimetric impact of simulated T&O displacements was significantly larger in the MRIG-CBT plans than in the point A plans. Either ± 3 mm T&O displacement or a ± 4.5 mm T&O reconstruction-uncertainty could cause greater than 10% dosimetric change for both point A plans and MRIG-CBT plans.