Semi-supervised SPECT segmentation using convolutional neural networks

1423 Objectives: Quantitative SPECT has been shown to provide improved lesion quantification compared to planar scintigraphy. Quantifying lesion uptake and metabolic tumor burden in SPECT rely heavily on accurate segmentation of bone and lesion structures. However, developing automated segmentation methods for SPECT images is challenging due to the limited spatial resolution and noise properties of SPECT images. Thus, manual segmentation is still the most common method used clinically for SPECT imaging, which is time-consuming and can suffer intra- and inter-observer variability. In this work, we present an automated segmentation algorithm that is based on convolutional neural networks (ConvNets). The proposed loss function effectively uses both intensity and shape information in a given image. We show that the proposed model trained using simulation images can produce accurate segmentation of previously-unseen patient images. Methods: This work introduces a novel semi-supervised loss function that is based on the classical Fuzzy C-means (FCM) algorithm. The proposed loss function incorporates the fundamental ideas of FCM, taking intensity information (mean intensities of each class) into consideration, and allowing for control of fuzzy overlap between segmentation classes with a user-controlled hyperparameter. The loss function is written as: L=LFCM+αLFCM-label, where LFCM=∑j∑kfqjk(y;θ)||yj-vk||2 is an unsupervised loss function that does not depend on segmentation labels, and LFCM-label=∑j∑kfqjk(y;θ)||gjk-1||2 is a supervised loss function; fjk(y;θ) is the ConvNet’s segmentation output of voxel j and class k; y and g denote the input SPECT image and the segmentation label, respectively; q controls the fuzzy overlap (q=2 used here); and α is a parameter that controls the weight between the unsupervised and the supervised terms. This loss function forces the ConvNet to leverage both the intensity distributions of the images and the available ground truth labels. The architecture of the ConvNet was a standard U-net. We implemented the model using Keras/Tensorflow on an NVIDIA Titan RTX GPU. The model was trained using 2D slices from 9 SPECT simulations (with different anatomical variations) for segmenting bone, lesion, and background. We tested the model on 12 clinical Tc-99m bone SPECT images. Dice coefficient (DSC) and surface DSC were used to as evaluation criteria. Results: We compared the proposed method with ConvNets trained using conventional dice and cross-entropy loss functions. Quantitatively, the proposed method outperformed the others by a significant margin, as shown in the table below. Qualitatively, the conventional supervised loss functions failed to yield usable segmentation results, which we believe is because the training images (SPECT simulations) did not capture all the lesion shape variations. In comparison, the proposed model produced reasonable segmentation results thanks to its ability to consider both shape information and intensity distributions within an image. Conclusion: We developed a semi-supervised loss function for SPECT segmentation using convolutional neural networks. The results demonstrated that our model trained using a dataset of simulated images was able to provide fast and robust segmentation on clinical SPECT images.