One challenge in applying deep learning to medical imaging is the lack of labeled data. Although large amounts of clinical data are available, acquiring labeled image data is difficult, especially for bone scintigraphy (i.e., 2D bone imaging) images. Bone scintigraphy images are generally noisy, and ground-truth or gold standard information from surgical or pathological reports may not be available. We propose a novel neural network model that can segment abnormal hotspots and classify bone cancer metastases in the chest area in a semisupervised manner. Our proposed model, called MaligNet, is an instance segmentation model that incorporates ladder networks to harness both labeled and unlabeled data. Unlike deep learning segmentation models that classify each instance independently, MaligNet utilizes global information via an additional connection from the core network. To evaluate the performance of our model, we created a dataset for bone lesion instance segmentation using labeled and unlabeled example data from 544 and 9,280 patients, respectively. Our proposed model achieved mean precision, mean sensitivity, and mean F1-score of 0.852, 0.856, and 0.848, respectively, and outperformed the baseline mask region-based convolutional neural network (Mask R-CNN) by 3.92%. Further analysis showed that incorporating global information also helps the model classify specific instances that require information from other regions. On the metastasis classification task, our model achieves a sensitivity of 0.657 and a specificity of 0.857, demonstrating its great potential for automated diagnosis using bone scintigraphy in clinical practice.

Pre-ablative pertechnetate WBS with neck and chest SPECT/CT has good correlation for the detection of post-surgical thyroid remnant, cervical node and discrete lung nodule metastasis as compared to (131)I WBS with SPECT/CT per-patient basis. Because (131)I therapeutic activity is desired base on metastatic site and less concerning about the number of lesions, pre-ablative (99m)Tc-pertechnetate WBS with SPECT/CT was a good alternative tool to avoid radioiodine stunning in post-surgical DTC patients.

No abstract

No abstract

P i c t u r e 1 . Ye l l o wi s h -b r o wn a n d s y mme t r i c a l l y d i s t r i b u t e d l e s i o n s o v e r b o t h u p p e r e y e l i d s a n d i n f r a o r b i t a l r e g i o n s . P i c t u r e 2 . Ab n o r ma l l y i n c r e a s e d r a d i o t r a c e r a c t i v i t y wa