Deep learning based automatic segmentation of metastasis hotspots in thorax bone SPECT images

Abstract

SPECT imaging has been identified as an effective medical modality for diagnosis, treatment, evaluation and prevention of a range of serious diseases and medical conditions. Bone SPECT scan has the potential to provide more accurate assessment of disease stage and severity. Segmenting hotspot in bone SPECT images plays a crucial role to calculate metrics like tumor uptake and metabolic tumor burden. Deep learning techniques especially the convolutional neural networks have been widely exploited for reliable segmentation of hotspots or lesions, organs and tissues in the traditional structural medical images (i.e., CT and MRI) due to their ability of automatically learning the features from images in an optimal way. In order to segment hotspots in bone SPECT images for automatic assessment of metastasis, in this work, we develop several deep learning based segmentation models. Specifically, each original whole-body bone SPECT image is processed to extract the thorax area, followed by image mirror, translation and rotation operations, which augments the original dataset. We then build segmentation models based on two commonly-used famous deep networks including U-Net and Mask R-CNN by fine-tuning their structures. Experimental evaluation conducted on a group of real-world bone SEPCT images reveals that the built segmentation models are workable on identifying and segmenting hotspots of metastasis in bone SEPCT images, achieving a value of 0.9920, 0.7721, 0.6788 and 0.6103 for PA (accuracy), CPA (precision), Rec (recall) and IoU, respectively. Finally, we conclude that the deep learning technology have the huge potential to identify and segment hotspots in bone SPECT images.

Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning

“…U-Net has been proven to possess the potential for bone metastases segmentation. Lin et al (19) built two deep learning networks based on U-Net and Mask R-CNN to segment hotspots in bone SPECT images for automatic assessment of metastasis. Their results showed that the U-Net-based model achieved better segmentation performance with a precision and recall value of 0.76 and 0.67 than the Mask R-CNN model (precision, 0.72; recall, 0.65).…”

“…The U-Net algorithm is one of the most commonly used deep learning-based convolutional neural networks (CNNs) ( 13 ), which shows potential in detection, segmentation, and classification of metastatic lesions on MRI images such as brain metastases ( 14 , 15 ) and liver metastases ( 16 ). Concerning the automated bone metastasis analysis using the deep learning technique, the research trend is mainly on BS ( 17 , 18 ) and single-photon emission computerized tomography (SPECT) images ( 19 , 20 ); less attention has been paid to the diagnosis of mpMRI ( 21 , 22 ). To this end, we intend to apply the 3D U-Net ( 23 ) algorithm for the segmentation of bone metastases on mpMRI images.…”

A muggles guide to deep learning wizardry

“…20 DL auto-segmentation of metastatic deposits on bone SPECT studies to improve staging and determining tumor burden. 21 DL and FDG PET/CT were used to predict (89% accuracy) treatment outcomes in patients with oral squamous cell carcinoma. 22 Detection of seizure foci on FDG brain PET was also enhanced, regardless of ictal or interictal status, with CNN based DL applications in pediatric patients with the DL framework.…”

Multiclass classification of whole‐body scintigraphic images using a self‐defined convolutional neural network with attention modules

“…Existing work mainly focuses on the development of CNN-based automated classification models for identifying bone lesions metastasized from multiple primary solid tumors [3,4], prostate cancer [5,[7][8][9], and breast cancer [6]. In our previous work, we developed CNN-based models to identify bone metastasis with thoracic SPECT scintigraphic images [12] and to segment the metastasized lesions from thoracic SPECT scintigraphic images [13].…”