Bone Spect Image Segmentation Based on the Hermite Transform

Vargas-Quintero, Lorena; Barba-J, Leiner; Calderon, Jose Alberto; Moreno, Cesar Torres

doi:10.1007/978-3-030-32040-9_14

Cited by 1 publication

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“…The size of a whole-body bone SPECT image is 256 × 1024. Currently, only few work has been done on segmenting whole- or partial-body bone structure by utilizing the region growing scheme [ 27 ], traditional neural network [ 28 ], convolutional neural network combined with active contour model [ 29 ], and clustering-based technique [ 30 ]. However, active contour model may suffer from edge leakage and high sensitivity to contour initialization [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%