Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. In addition, multi-modal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to miss some imaging modalities in clinical practice. In this paper, we present a novel brain tumor segmentation algorithm with missing modalities. Since it exists a strong correlation between multi-modalities, a correlation model is proposed to specially represent the latent multi-source correlation. Thanks to the obtained correlation representation, the segmentation becomes more robust in the case of missing modality. First, the individual representation produced by each encoder is used to estimate the modality independent parameter. Then, the correlation model transforms all the individual representations to the latent multi-source correlation representations. Finally, the correlation representations across modalities are fused via attention mechanism into a shared representation to emphasize the most important features for segmentation. We evaluate our model on BraTS 2018 and BraTS 2019 dataset, it outperforms the current state-of-the-art methods and produces robust results when one or more modalities are missing.

In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multimodality segmentation network with a correlation constraint. Our network includes N model-independent encoding paths with N image sources, a correlation constrain block, a feature fusion block, and a decoding path. The model independent encoding path can capture modality-specific features from the N modalities. Since there exists a strong correlation between different modalities, we first propose a linear correlation block to learn the correlation between modalities, then a loss function is used to guide the network to learn the correlated features based on the linear correlation block. This block forces the network to learn the latent correlated features which are more relevant for segmentation. Considering that not all the features extracted from the encoders are useful for segmentation, we propose to use dual attention based fusion block to recalibrate the features along the modality and spatial paths, which can suppress less informative features and emphasize the useful ones. The fused feature representation is finally projected by the decoder to obtain the segmentation result. Our experiment results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate the effectiveness of our proposed method.

MOTIVATION: Next-generation sequencing has become the go-to standard method for the detection of single-nucleotide variants in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies. These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of unique molecular identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artefactual variants and accurately call low-frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. RESULTS: We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that do not rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions. AVAILABILITY AND IMPLEMENTATION: The entire pipeline is available at https://gitlab.com/vincent-sater/umi-varcal-master under MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lymphoma detection and segmentation from PET images are critical tasks for cancer staging and treatment monitoring. However, it is still a challenge owing to the complexities of lymphoma PET data themselves, and the huge memory requirements for 3D volume data. In this work, a prior knowledge integrated scheme based on DBSCAN and GAs is proposed to detect and segment lymphomas in 3D PET images. To reduce memory requirements and add more feature information, billions of voxels in 3D volume data are first aggregated into supervoxels. Then, such supervoxels serve as basic data units for further clustering the supervoxels by using DBSCAN algorithm, in which a new similarity measure based on prior knowledge is proposed. Meanwhile, a genetic algorithm is used to search the most appropriate parameters of DBSCAN to obtain the optimal clustering results. Finally, a series of comparison experiments among various feature attributes and various similarity functions are performed, and the results are encouraging, and show that the proposed scheme by intergrating the prior knowledge of organ distributions can achieve better performance than traditional methods.

Due to the poor resolution and low signal-to-noise ratio in PET images, and especially to the wide variation in size, shape, site and SUV value among different patients or even for the same patient, lymphoma segmentation in 3D PET Images is still a challenging task in the field of medical image processing. In this work, a novel non-self background-based data enhancement method is proposed for the deep learning-based lymphoma segmentation problem. Firstly, a lymphoma pool with 1991 lymphoid lesions is created. Then, some lymphomas from the lymphoma pool are randomly selected and integrated into their non-self images of the patients according to their respective coordinates when training networks. Finally, a series of comparison experiments among various network models and methods are conducted to verify the effectiveness of the proposed method. The results indicated that the proposed method was promising, and could obtain better comprehensive performance than other methods without any data enhancements for the lymphoma segmentation problems.

Precise delineation of target tumor is a key factor to ensure the effectiveness of radiation therapy. While hybrid positron emission tomography-computed tomography (PET-CT) has become a standard imaging tool in the practice of radiation oncology, many existing automatic/semi-automatic methods still perform tumor segmentation on mono-modal images. In this paper, a co-clustering algorithm is proposed to concurrently segment 3D tumors in PET-CT images, considering that the two complementary imaging modalities can combine functional and anatomical information to improve segmentation performance. The theory of belief functions is adopted in the proposed method to model, fuse, and reason with uncertain and imprecise knowledge from noisy and blurry PET-CT images. To ensure reliable segmentation for each modality, the distance metric for the quantification of clustering distortions and spatial smoothness is iteratively adapted during the clustering procedure. On the other hand, to encourage consistent segmentation between different modalities, a specific context term is proposed in the clustering objective function. Moreover, during the iterative optimization process, clustering results for the two distinct modalities are further adjusted via a belief-functions-based information fusion strategy. The proposed method has been evaluated on a data set consisting of 21 paired PET-CT images for non-small cell lung cancer patients. The quantitative and qualitative evaluations show that our proposed method performs well compared with the state-of-the-art methods.

While hybrid PET/CT scanner is becoming a standard imaging technique in clinical oncology, many existing methods still segment tumor in mono-modality without consideration of complementary information from another modality. In this paper, we propose an unsupervised 3-D method to automatically segment tumor in PET images, where anatomical knowledge from CT images is included as critical guidance to improve PET segmentation accuracy. To this end, a specific context term is proposed to iteratively quantify the conflicts between PET and CT segmentation. In addition, to comprehensively characterize image voxels for reliable segmentation, informative image features are effectively selected via an unsupervised metric learning strategy. The proposed method is based on the theory of belief functions, a powerful tool for information fusion and uncertain reasoning. Its performance has been well evaluated by real-patient PET/CT images.

We propose a patient-specific model based on partial differential equation to predict the evolution of lung tumors during radiotherapy. The evolution of tumor cell density is formulated by three terms: 1) advection describing the advective flux transport of tumor cells, 2) proliferation representing the tumor cell proliferation modeled as Gompertz differential equation, and 3) treatment quantifying the radiotherapeutic efficacy from linear quadratic formulation. We consider that tumor cell density variation can be derived from positron emission tomography images, the novel idea is to model the advection term by calculating 3D optical flow field from sequential images. To estimate patient-specific parameters, we propose an optimization between the predicted and observed images, under a global constraint that the tumor volume decreases exponentially as radiation dose increases. A thresholding on the predicted tumor cell densities is then used to define tumor contours, tumor volumes and maximum standardized uptake values (SUVmax). Results obtained on seven patients show a satisfying agreement between the predicted tumor contours and those drawn by an expert.