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Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as CBCT, to meet the online demands of plan evaluation and adaptation. Deep learning (DL)-based automatic segmentation has gained great success in segmenting planning CT, but its applications to CBCT yielded inferior results due to the low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT segmentation, we propose a registration-guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models. The registration algorithm generates initial contours, which were used as guidance by DL model to obtain accurate final segmentations. We had two implementations the proposed framework--Rig-RgDL (Rig for rigid body) and Def-RgDL (Def for deformable)--with rigid body (RB) registration or deformable image registration (DIR) as the registration algorithm respectively and U-Net as DL model architecture. The two implementations of RgDL framework were trained and evaluated on seven OARs in an institutional clinical Head and Neck (HN) dataset. Compared to the baseline approaches using the registration or the DL alone, RgDL achieved more accurate segmentation, as measured by higher mean Dice similarity coefficients (DSC) and other distance-based metrics. Rig-RgDL achieved a DSC of 84.5% on seven OARs on average, higher than RB or DL alone by 4.5% and 4.7%. The DSC of Def-RgDL is 86.5%, higher than DIR or DL alone by 2.4% and 6.7%. The inference time took by the DL model to generate final segmentations of seven OARs is less than one second in RgDL. The resulting segmentation accuracy and efficiency show the promise of applying RgDL framework for online ART., 16 pages, 6 figures

Breast ultrasound (US) is an effective imaging modality for breast cancer detec-tion and diagnosis. The structural characteristics of breast lesion play an im-portant role in Computer-Aided Diagnosis (CAD). In this paper, a novel struc-ture-aware triplet path networks (SATPN) was designed to integrate classifica-tion and two image reconstruction tasks to achieve accurate diagnosis on US im-ages with small training dataset. Specifically, we enhance clinically-approved breast lesion structure characteristics though converting original breast US imag-es to BIRADS-oriented feature maps (BFMs) with a distance-transformation coupled Gaussian filter. Then, the converted BFMs were used as the inputs of SATPN, which performed lesion classification task and two unsupervised stacked convolutional Auto-Encoder (SCAE) networks for benign and malignant image reconstruction tasks, independently. We trained the SATPN with an alter-native learning strategy by balancing image reconstruction error and classification label prediction error. At the test stage, the lesion label was determined by the weighted voting with reconstruction error and label prediction error. We com-pared the performance of the SATPN with TPN using original image as input and our previous developed semi-supervised deep learning methods using BFMs as inputs. Experimental results on two breast US datasets showed that SATPN ranked the best among the three networks, with classification accuracy around 93.5%. These findings indicated that SATPN is promising for effective breast US lesion CAD using small datasets., Comment: arXiv admin note: substantial text overlap with arXiv:1904.01076