In secondary hyperparathyroidism (SHPT) disease, preoperatively localizing hyperplastic parathyroid glands is crucial in the surgical procedure. These glands can be detected via the dual-modality imaging technique single-photon emission computed tomography/computed tomography (SPECT/CT) since it has high sensitivity and provides an accurate location. However, due to possible low-uptake glands in SPECT images, manually labeling glands is challenging, not to mention automatic label methods. In this work, we present a deep learning method with a novel fusion network to detect hyperplastic parathyroid glands in SPECT/CT images. Our proposed fusion network follows the convolutional neural network (CNN) with a three-pathway architecture that extracts modality-specific feature maps. The fusion network, composed of the channel attention module, the feature selection module, and the modality-specific spatial attention module, is designed to integrate complementary anatomical and functional information, especially for low-uptake glands. Experiments with patient data show that our fusion method improves performance in discerning low-uptake glands compared with current fusion strategies, achieving an average sensitivity of 0.822. Our results prove the effectiveness of the three-pathway architecture with our proposed fusion network for solving the glands detection task. To our knowledge, this is the first study to detect abnormal parathyroid glands in SHPT disease using SPECT/CT images, which promotes the application of preoperative glands localization.

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical applications. Deep convolutional neural networks have become the state-of-the-art in PET tumor segmentation. The normalization process is one of the key components for accelerating network training and improving the performance of the network. However, existing normalization methods either introduce batch noise into the instance PET image by calculating statistics on batch level or introduce background noise into every single pixel by sharing the same learnable parameters spatially. In this paper, we proposed an attentive transformation (AT)-based normalization method for PET tumor segmentation. We exploit the distinguishability of breast tumor in PET images and dynamically generate dedicated and pixel-dependent learnable parameters in normalization via the transformation on a combination of channel-wise and spatial-wise attentive responses. The attentive learnable parameters allow to re-calibrate features pixel-by-pixel to focus on the high-uptake area while attenuating the background noise of PET images. Our experimental results on two real clinical datasets show that the AT-based normalization method improves breast tumor segmentation performance when compared with the existing normalization methods.

Intra/inter switching-based error resilient video coding effectively enhances the robustness of video streaming when transmitting over error-prone networks. But it has a high computation complexity, due to the detailed end-to-end distortion prediction and brute-force search for rate-distortion optimization. In this article, a Low Complexity Mode Switching based Error Resilient Encoding (LC-MSERE) method is proposed to reduce the complexity of the encoder through a deep learning approach. By designing and training multi-scale information fusion-based convolutional neural networks (CNN), intra and inter mode coding unit (CU) partitions can be predicted by the networks rapidly and accurately, instead of using brute-force search and a large number of end-to-end distortion estimations. In the intra CU partition prediction, we propose a spatial multi-scale information fusion based CNN (SMIF-Intra). In this network a shortcut convolution architecture is designed to learn the multi-scale and multi-grained image information, which is correlated with the CU partition. In the inter CU partition, we propose a spatial-temporal multi-scale information fusion-based CNN (STMIF-Inter), in which a two-stream convolution architecture is designed to learn the spatial-temporal image texture and the distortion propagation among frames. With information from the image, and coding and transmission parameters, the networks are able to accurately predict CU partitions for both intra and inter coding tree units (CTUs). Experiments show that our approach significantly reduces computation time for error resilient video encoding with acceptable quality decrement.