Query: Liver tumor  MICCAI

title: Semantic Segmentation of Liver Tumor in Contrast enhanced Hepatic CT by Using Deep Learning with Hessian based Enhancer with Small Training Dataset Size
abstract: Deep learning requires a large dataset for training, but collecting and annotating such a large dataset are time consuming and often very difficult. In this study, we proposed a 3D massive training artificial neural network (MTANN) incorporated with a Hessian based enhancer to achieve a high performance of our MTANN model with small training datasets. Contrast enhanced CT scans of 42 patients with 194 liver tumors from the Liver Tumor Segmentation (LiTS) Benchmark database were used in this study. MTANN models were trained with: 14 and 28 tumors. The remaining 24 patients with 59 tumors were applied to the trained models for evaluation. Our method achieved a Dice of 0.703 with a training set of 14 tumors. The performance was comparable to the best performance in the MICCAI 2017 competition with less than one tenth of training cases. Our method would be essential in applications where a large training set is not available.

title: miR 361 3p Regulates Liver Tumor initiating Cells Expansion and Chemo resistance
abstract: Increasing evidence shows that liver tumor initiating cells (T ICs) closely associated with the progression, metastasis, recurrence and chemo resistance of hepatocellular carcinoma (HCC) However, the underlying mechanism for the propagation of liver T ICs remains unclear. Here we show that miR 361 3p is upregulated in liver T ICs. Knockdown of miR 361 3p impairs the self renewal and tumorigenicity liver T ICs. Conversely, forced miR 361 3p expression enhances the self renewal and tumorigenicity liver T ICs. Mechanistically, miR 361 3p directly targets SOX1 via binding its 3' UTR in liver T ICs. Moreover, miR 361 3p knockdown hepatoma cells are more sensitive to cisplatin or sorafenib treatment. Clinical cohort analysis demonstrates that miR 361 3p low HCC patients are benefited from TACE (transcatheter arterial chemoembolization) or sorafenib treatment. In conclusion, our findings revealed the crucial role of the miR 361 3p in liver T IC expansion and TACE or sorafenib response, rendering miR 361 3p an optimal target for the prevention and intervention in HCC.

title: Liver tumor segmentation using 2.5D UV Net with multi scale convolution
abstract: Liver tumor segmentation networks are generally based on U shaped encoder decoder network with 2D or 3D structure. However, 2D networks lose the inter layer information of continuous slices and 3D networks might introduce unacceptable parameters for GPU memory. As a result, 2.5D networks were proposed to balance the memory consumption and 3D context. Different from the canonical 2.5D design, which utilizes a 2D network combined with RNN, we propose a new 2.5D design called UV Net to encode the inter layer information in the context of 3D convolution, and reconstruct the high resolution results with 2D deconvolution. At the same time, the multi scale convolution structure enables multi scale feature extraction without extra computational cost, which effectively mines structured information, reduces information redundancy, strengthens independent features, and makes feature dimension sparse, to enhance network capacity and efficiency. Combined with the proposed preprocessing method of removing mean energy, UV Net significantly outperforms the existing methods in liver tumor segmentation and especially improves the segmentation accuracy of small objects on the LiTS2017 dataset.

title: CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging
abstract: Artificial intelligence (AI) has made various developments in the image segmentation techniques in the field of medical imaging. This article presents a liver tumor CT image segmentation method based on AI medical imaging based technology. This study proposed an artificial intelligence based K means clustering (KMC) algorithm which is further compared with the region growing (RG) method. In this study, 120 patients with liver tumors in the Post Graduate Institute of Medical Education Research Hospital, Chandigarh, India, were selected as the research objects, and they were classified according to liver function (Child Pugh) with 58 cases in grade A and 62 cases in grade B. The experimentation indicates that liver tumor showed low density on plain CT scan, moderate enhancement in the arterial phase of the enhanced scan, and low density filling defect in the involved blood vessel in the portal venous phase (PVP) It was observed that the CT examination is more sensitive to liver metastasis than hepatocellular carcinoma The outcomes obtained depict the good deposition effect of lipiodol chemotherapy emulsion (LCTE) in the contrast group with rich blood type accounted for 53.14% and the patients with the poor blood type accounted for 25.73% showed poor deposition effect. The comparison with the state of the art method reveals that the segmentation effect of the KMC algorithm is better than that of the conventional RG method.

title: Prediction of Early Recurrence After Surgery for Liver Tumor (ERASL) An International Validation of the ERASL Risk Models
abstract: This study aimed to assess the performance of the pre and postoperative early recurrence after surgery for liver tumor (ERASL) models at external validation. Prediction of early hepatocellular carcinoma (HCC) recurrence after resection is important for individualized surgical management. Recently, the preoperative (ERASL pre) and postoperative (ERASL post) risk models were proposed based on patients from Hong Kong. These models showed good performance although they have not been validated to date by an independent research group. This international cohort study included 279 patients from the Netherlands and 392 patients from Japan. The patients underwent first time resection and showed a diagnosis of HCC on pathology. Performance was assessed according to discrimination (concordance [C] statistic) and calibration (correspondence between observed and predicted risk) with recalibration in a Weibull model. The discriminatory power of both models was lower in the Netherlands than in Japan (C statistic, 0.57 [95% confidence interval {CI} 0.52 0.62] vs 0.69 [95% CI 0.65 0.73] for the ERASL pre model and 0.62 [95% CI 0.57 0.67] vs 0.70 [95% CI 0.66 0.74] for the ERASL post model) whereas their prognostic profiles were similar. The predictions of the ERASL models were systematically too optimistic for both cohorts. Recalibrated ERASL models improved local applicability for both cohorts. The discrimination of ERASL models was poorer for the Western patients than for the Japanese patients, who showed good performance. Recalibration of the models was performed, which improved the accuracy of predictions. However, in general, a model that explains the East West difference or one tailored to Western patients still needs to be developed.

title: DefED Net: Deformable Encoder Decoder Network for Liver and Liver Tumor Segmentation
abstract: Deep convolutional neural networks have been widely used for medical image segmentation due to their superiority in feature learning. Although these networks are successful for simple object segmentation tasks, they suffer from two problems for liver and liver tumor segmentation in CT images. One is that convolutional kernels of fixed geometrical structure are unmatched with livers and liver tumors of irregular shapes. The other is that pooling and strided convolutional operations easily lead to the loss of spatial contextual information of images. To address these issues, we propose a deformable encoder decoder network (DefED Net) for liver and liver tumor segmentation. The proposed network makes two contributions. The first is that the deformable convolution is used to enhance the feature representation capability of DefED Net, which can help the network to learn convolution kernels with adaptive spatial structuring information. The second is that we design a Ladder atrous spatial pyramid pooling module using multi scale dilation rate (Ladder ASPP) and apply the module to learn better context information than the atrous spatial pyramid pooling (ASPP) for CT image segmentation. The proposed DefED Net is evaluated on two public benchmark datasets, the LiTS and the 3DIRCADb. Experiments demonstrate that the DefED Net has better capability of feature representation as well as provides higher accuracy on liver and liver tumor segmentation than stateof the art networks. The available code of DefED Net we propose can be found from https:/github.com/SUST reynole/DefED

title: Weakly Supervised teacher Student network for liver tumor segmentation from non enhanced images
abstract: Accurate liver tumor segmentation without contrast agents (non enhanced images) avoids the contrast agent associated time consuming and high risk, which offers radiologists quick and safe assistance to diagnose and treat the liver tumor. However, without contrast agents enhancing, the tumor in liver images presents low contrast and even invisible to naked eyes. Thus the liver tumor segmentation from non enhanced images is quite challenging. We propose a Weakly Supervised Teacher Student network (WSTS) to address the liver tumor segmentation in non enhanced images by leveraging additional box level labeled data (labeled with a tumor bounding box) WSTS deploys a weakly supervised teacher student framework (TCH ST) namely, a Teacher Module learns to detect and segment the tumor in enhanced images during training, which facilitates a Student Module to detect and segment the tumor in non enhanced images independently during testing. To detect the tumor accurately, the WSTS proposes a Dual strategy DRL (DDRL) which develops two tumor detection strategies by creatively introducing a relative entropy bias in the DRL. To accurately predict a tumor mask for the box level labeled enhanced image and thus improve tumor segmentation in non enhanced images, the WSTS proposes an Uncertainty Sifting Self Ensembling (USSE) The USSE exploits the weakly labeled data with self ensembling and evaluates the prediction reliability with a newly designed Multi scale Uncertainty estimation. WSTS is validated with a 2D MRI dataset, where the experiment achieves 83.11% of Dice and 85.12% of Recall in 50 patient testing data after training by 200 patient data (half amount data is box level labeled) Such a great result illustrates the competence of WSTS to segment the liver tumor from non enhanced images. Thus, WSTS has excellent potential to assist radiologists by liver tumor segmentation without contrast agents.

title: PEComa A Rare Liver Tumor
abstract: PEComa (perivascular epithelioid cell tumor) is a rare liver tumor. Decisions regarding patient management are currently based on a few small case series. The aim of this study was to report the clinicopathological features of PEComa in order to provide guidance for management, complemented by our own experience. This retrospective observational study included all patients with PEComa who underwent surgical treatment in two departments between 2002 and 2020. A total of 20 patients were diagnosed with PEComa following histopathological examination. The age of the patients ranged from 21 to 73 years. The majority of patients were women (85% In most patients, the tumors were incidental. In diagnostic studies, PEComas with high arterial vascularization have been described. Liver resection was the treatment of choice. There was only one postoperative complication. During histopathological evaluation, tumors were composed mostly of epithelioid cells, rarely with spindle cell components, thick walled vessels, and adipocytes in different proportions. Melanocytic markers (HMB45, MelanA) and at least one smooth muscle marker were expressed in all tumors. Features suggestive of malignancy were found in three cases. In conclusion, PEComa is a rare liver tumor that is usually diagnosed incidentally. In radiological studies, tumors with high arterial vascularization are observed. Liver resection is the treatment of choice.

title: MAGAN: Mask Attention Generative Adversarial Network for Liver Tumor CT Image Synthesis
abstract: For deep learning, the size of the dataset greatly affects the final training effect. However, in the field of computer aided diagnosis, medical image datasets are often limited and even scarce. We aim to synthesize medical images and enlarge the size of the medical image dataset. In the present study, we synthesized the liver CT images with a tumor based on the mask attention generative adversarial network (MAGAN) We masked the pixels of the liver tumor in the image as the attention map. And both the original image and attention map were loaded into the generator network to obtain the synthesized images. Then, the original images, the attention map, and the synthesized images were all loaded into the discriminator network to determine if the synthesized images were real or fake. Finally, we can use the generator network to synthesize liver CT images with a tumor. The experiments showed that our method outperformed the other state of the art methods and can achieve a mean peak signal to noise ratio (PSNR) of 64.72 dB. All these results indicated that our method can synthesize liver CT images with a tumor and build a large medical image dataset, which may facilitate the progress of medical image analysis and computer aided diagnosis.  https:/www.researchsquare.com/article/rs 41685/v1.

title: PA ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi phase CT Images
abstract: Liver tumor segmentation is a crucial prerequisite for computer aided diagnosis of liver tumors. In the clinical diagnosis of liver tumors, radiologists usually examine multi phase CT images as these images provide abundant and complementary information of tumors. However, most known automatic segmentation methods extract tumor features from CT images merely of a single phase, in which valuable multi phase information is ignored. Therefore, it is highly demanded to develop a method effectively incorporating multi phase information for automatic and accurate liver tumor segmentation.  In this paper, we propose a phase attention residual network (PA ResSeg) to model multi phase features for accurate liver tumor segmentation. A phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intra phase attention (Intra PA) module and an inter phase attention (Inter PA) module to capture channel wise self dependencies and cross phase interdependencies, respectively. Thus it enables the network to learn more representative multi phase features by refining the PV features according to the channel dependencies and recalibrating the ART features based on the learned interdependencies between phases. We propose a PA based multi scale fusion (MSF) architecture to embed the PA blocks in the network at multiple levels along the encoding path to fuse multi scale features from multi phase images. Moreover, a 3D boundary enhanced loss (BE loss) is proposed for training to make the network more sensitive to boundaries.  To evaluate the performance of our proposed PA ResSeg, we conducted experiments on a multi phase CT dataset of focal liver lesions (MPCT FLLs) Experimental results show the effectiveness of the proposed method by achieving a dice per case (DPC) of 0.7787, a dice global (DG) of 0.8682, a volumetric overlap error (VOE) of 0.3328 and a relative volume difference (RVD) of 0.0443 on the MPCT FLLs. Furthermore, to validate the effectiveness and robustness of PA ResSeg, we conducted extra experiments on another multi phase liver tumor dataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637 and a RVD of 0.0163. The proposed method shows its robustness and generalization capability in different datasets and different backbones.  The study demonstrates that our method can effectively model information from multi phase CT images to segment liver tumors and outperforms other state of the art methods. The PA based MSF method can learn more representative multi phase features at multiple scales and thereby improve the segmentation performance. Besides, the proposed 3D BE loss is conducive to tumor boundary segmentation by enforcing the network focus on boundary regions and marginal slices. Experimental results evaluated by quantitative metrics demonstrate the superiority of our PA ResSeg over the best known methods.