39 results about "Abdominal ct" patented technology

The invention relates to the technical field of medical image processing, in particular to a colorectal cancer auxiliary staging method and system based on a CT image, and the method comprises the following steps: obtaining an abdominal CT image of a patient with colorectal cancer, and obtaining CT image data containing focus labeling information; preprocessing the CT image data containing the focus labeling information, and constructing a CT image data set; constructing an improved UNet network model based on the UNet network architecture, and performing training optimization on the improved UNet network model by adopting the CT image data set to obtain a trained improved UNet network model; and inputting a to-be-staging CT image into the trained improved UNet network model, and outputting a colorectal cancer T staging result corresponding to the to-be-staging CT image. According to the technical scheme of the invention, precise focus segmentation and automatic classification of T stages of the colorectal cancer CT image are realized.

The invention discloses an abdominal CT image multi-view fusion method based on HU physical characteristic guidance, and relates to the technical field of medical image processing. The method comprises the following steps: firstly, constructing a three-dimensional HU volume, generating a semantic mask covering the whole HU range as physical prior, and obtaining two-dimensional slice sequences in the axial direction, the coronal direction and the sagittal direction through three-view projection; on the basis, multi-scale deformable alignment, HU-perceived deformation field fine correction, cross-view attention fusion and regional differentiation decision fusion are sequentially carried out, HU-perceived high-frequency residual enhancement and local contrast self-adaptive processing are carried out on two-dimensional slices, and finally a denser CT slice sequence is generated. According to the method, HU physical partition constraint is introduced in the multi-view alignment and fusion process, abnormal deformation and high-density structure distortion of a gas region are effectively inhibited, and geometric consistency and visual stability of an abdominal CT image in the interpolation reconstruction process are improved.

The invention discloses a CT-based osteoporosis intelligent diagnosis method and system, and relates to the technical field of medical image diagnosis, and the method comprises the steps: carrying out the positioning of a lumbar region in an abdomen or thoracic and abdominal CT image through a deep learning model; digitally modeling the shape and the internal structure of the vertebral body; and automatic diagnosis of osteoporosis is completed. According to the method, full-automatic positioning, accurate segmentation and intelligent diagnosis of osteoporosis of the lumbar region in a conventional abdomen or thoracic and abdominal CT image are realized, the accuracy and efficiency of diagnosis are remarkably improved, and the problems of strong subjectivity and poor repeatability caused by dependence on manual sketching or simple threshold segmentation in a traditional method are solved; the method can be directly operated based on clinical conventional CT data, is beneficial to large-scale osteoporosis screening and early intervention, and has remarkable clinical popularization significance and technical progress value.

The invention belongs to the field of medical image processing and artificial intelligence, and provides a colon cancer CT image segmentation method and system fusing individualized features, and the method comprises the steps: obtaining the abdomen CT images of a historical patient and a target patient; obtaining structured individual feature information related to the patient, wherein the individual feature information comprises age, gender, colon cancer family history and intestinal medical history information; preprocessing the abdomen CT image; and training an image segmentation model by using the preprocessed abdominal CT image of the historical patient, segmenting the preprocessed abdominal CT image of the target patient by using the trained image segmentation model meeting the segmentation precision requirement, and generating a colon cancer focus segmentation image conforming to the individual feature difference of the target patient. According to the method, the structured patient information is utilized to guide the model network to adjust the feature response, so that the segmentation precision and individual adaptability of the model are improved, and the segmentation accuracy is further improved.

The invention relates to the technical field of medical image segmentation, and discloses an abdominal medical image multi-organ segmentation method based on text guidance and MFF. The method comprises the following steps: firstly, constructing a lightweight image encoder, and extracting multi-scale image features of an abdominal CT image; secondly, a text encoder based on Transform is adopted to extract high-dimensional semantic features of the input text, and precise modeling of medical text semantics is achieved; then designing an image-text feature fusion module, and realizing deep interaction and semantic alignment of image and text features through a self-attention and cross-modal attention mechanism; and finally, providing a multi-scale feature decoder combined with a jump connection mechanism, fusing the multi-layer features and the multi-modal fusion features of the encoder, and outputting an abdomen multi-organ segmentation result and a target score of each organ. According to the method, the accuracy and robustness of complex abdominal organ segmentation can be effectively improved, and the multi-modal information fusion capability of the model is remarkably optimized while light weight is ensured.

The invention relates to the technical field of artificial intelligence, and provides an artificial intelligence assisted gallbladder image automatic detection method and system and electronic equipment, and the method comprises the steps: generating a corresponding generative image for an original abdomen CT image, and reconstructing the CT image of a gallbladder in a contraction state into a gallbladder-filled CT image; performing part identification on the generative image to determine whether a gallbladder part exists in the generative image; if the gall bladder part exists in the generative image, carrying out part identification on the original abdomen CT image to obtain a contour area of the gall bladder part; calculating the gallbladder volume according to the contour area of the gallbladder part, and detecting whether the gallbladder size is abnormal or not according to the gallbladder volume to obtain a gallbladder detection result. According to the invention, the automatic detection of the gall bladder image is realized, the rapid analysis of the CT image can be assisted, the influence of the current state of the gall bladder on the recognition accuracy of the gall bladder part is effectively avoided, and a more accurate gall bladder image detection result is rapidly obtained.

The application provides a liver fibrosis intelligent evaluation method and system based on an abdominal CT image, and the method comprises the following steps: extracting a liver region image from the abdominal CT image to construct 3D body data of the liver region; performing region segmentation on the body data to form a whole liver mask, a target region mask, and calculating the proportion of the volume of the target region to the volume of the liver, the target region being a left outer lobe upper segment and a left outer lobe lower segment region; when the proportion of the volume of the target region to the volume of the liver meets a preset threshold interval, extracting effective surface vertices of the target region and constructing a surface mesh; marking the nodule vertices in the surface mesh and weighting the heights of the nodule vertices, performing quadratic polynomial surface fitting on the weighted surface mesh to obtain a smooth reference surface; performing multi-dimensional surface feature extraction on the real surface mesh based on the smooth reference surface; and performing liver surface nodule scoring and liver fibrosis staging evaluation according to the multi-dimensional surface features.

The invention provides an intelligent evaluation method and system for hepatic fibrosis based on an abdominal CT image, and the method comprises the steps: extracting a liver region image from the abdominal CT image, and constructing the 3D volume data of a liver region; performing region segmentation on the volume data to form a whole liver mask and a target region mask, and calculating the proportion of the volume of a target region to the volume of the liver, the target region being a left outer lobe upper section region and a left outer lobe lower section region; when the proportion of the volume of the target area to the volume of the liver meets a preset threshold interval, extracting effective surface vertexes of the target area and constructing a surface grid; nodule vertexes in the surface grid are marked, the height of each nodule vertex is weighted, and quadratic polynomial surface fitting is carried out on the weighted surface grid to obtain a smooth reference surface; performing multi-dimensional surface feature extraction on the real surface grid based on the smooth reference surface; and performing liver surface nodule scoring and liver fibrosis staging evaluation according to the multi-dimensional surface features.

This invention discloses a pancreas segmentation method based on the WEUnet network, belonging to the field of pancreas segmentation technology. It solves the problem of unclear edges in pancreas segmentation. The method includes the following steps: S1, acquiring an abdominal CT image and inputting it into the WEUnet network; S2, performing five consecutive convolutional encoding processes on the abdominal CT image through the encoding module to obtain the encoded visual feature maps output by each layer of the encoding module; S3, inputting the bottom-level encoded visual feature map into the decoding module to obtain the bottom-level decoded prediction feature map; S4, performing four consecutive convolutional decoding processes on the bottom-level decoded prediction feature map through the decoding module to obtain the top-level decoded prediction feature map; S5, obtaining the pancreas image based on the top-level decoded prediction feature map. This network effectively solves the problems of pancreas deformation, unclear edges, and class imbalance, and further improves the segmentation index.

The application discloses a kind of multi-modal gastric cancer risk prediction method and system based on gate attention mechanism, it is related to gastric cancer risk prediction technical field, including, to abdominal CT image, gastric organ recognition result and health text information preprocessing, fusion standardization CT image and gastric organ recognition result, construct organ priori enhancement image.To organ priori enhancement image and health text information extract image feature and text feature, respectively calculate self-attention and cross-attention, according to self-attention entropy and cross-attention peak value generation image and the gate fusion coefficient of text, the weighted fusion of the output of two kinds of attention obtains gate fusion feature, and then calculates self-attention and cross-attention and is added in equal proportion to obtain fusion feature, and the risk of target sample suffering from gastric cancer is classified and output.The application makes image feature and text feature complement each other, improves the accuracy and stability of gastric cancer risk prediction, is suitable for health management auxiliary, and is not used as clinical diagnosis basis.

The application discloses a method for abdominal CT image segmentation based on branch growth neural network architecture search, comprising the following steps: constructing a search space network based on local width branch and global depth branch; alternately connecting the local width branch and the global depth branch to the growable down-sampling layer of an encoder based on the search space network to construct a grown candidate network; training and testing the grown candidate network to obtain an image segmentation model; inputting the abdominal CT image data to be tested into the image segmentation model to complete abdominal CT image segmentation. The application improves the accuracy of abdominal CT image segmentation.

The application discloses a pancreas image segmentation method and system based on deep learning. The method comprises the following steps: acquiring an abdominal CT image sequence and performing window width and window level adjustment, resampling and region of interest cropping; loading pre-trained and fixed weight encoder weight and joint segmentation network model parameter, performing single forward propagation inference on the preprocessed image to obtain a segmentation probability map and a predicted shape vector; post-processing the segmentation probability map to output a three-dimensional pancreas segmentation mask; loading shape prior knowledge base, and combining the predicted shape vector to perform offline confidence evaluation on the segmentation result. The application effectively solves the segmentation fracture problem caused by the fuzzy pancreas boundary by fusing global shape prior constraints, and quantifies the reliability of the result through the confidence evaluation mechanism, thereby improving the precision and clinical applicability of pancreas segmentation.

This invention discloses a method and system for assessing postoperative abdominal organ ischemia risk based on image analysis, belonging to the field of image analysis technology. The method includes acquiring enhanced abdominal CT images of the target patient and outputting them after standardization processing; employing an improved 3D U-Net++ architecture with multi-task collaborative learning to jointly segment the target patient's organs and target regions, integrating geometric priors and topological inference mechanisms; its key technical points are: using multi-task segmentation to focus RPPR calculation on the real ischemic area, avoiding average dilution of the signal across all organs, and topological correction to ensure that vascular features reflect the real anatomy; furthermore, through the joint analysis of low-perfusion area prediction masks and vascular VTIF, it reveals two ischemic subtypes: structural occlusion and functional hypoperfusion, promoting the individualization of clinical intervention strategies and defining the necessary vascular intervention or conservative treatment, making the overall solution both innovative and clinically applicable.

The present application relates to a liver CT image segmentation method based on global self-attention and multi-scale feature fusion, belonging to the technical field of medical image processing. The present application comprises the following steps: (1) obtaining an abdominal CT data set and performing pretreatment; (2) using a ResNeXt convolutional neural network to extract multi-scale features and introducing multi-scale spatial information; (3) using the multi-scale features through a global self-attention module to obtain global self-attention fusion features; (4) extracting features through an improved convolution module for the fusion features, and finally up-sampling to obtain a segmentation result. The method is verified based on the LiTS public data set, and the average Dice value of the overlapping area of the segmentation result and the true segmentation reaches 96.4%, which is 4.3% higher than that of the classic model UNet.

The invention provides an arm fixing and thoracic and abdominal cavity external expansion device suitable for thoracic and abdominal CT scanning, which is based on an examination bed for thoracic and abdominal CT scanning and comprises a base platform, an upper limb supporting component, an upper limb fixing component, a headrest and a scapula wedge pad, the base platform is horizontally arranged on the examination bed, an upper limb fixing part, a headrest and a scapula wedge pad are arranged on the base platform, and the upper limb fixing part is located at the front end of the base platform and comprises a fixing wrist strap; the headrest and the upper limb fixing part are correspondingly arranged, the upper limb supporting part comprises a left supporting arm, a right supporting arm and an elastic band, and the left supporting arm and the right supporting arm are arranged on the two sides of the base platform in a mirror symmetry mode respectively. When the arm fixing and thoracic and abdominal cavity external expansion device suitable for thoracic and abdominal CT scanning is used, upper limb uplifting, stable fixing, thorax abduction and thyroid protection can be achieved. According to the invention, the problem of artifacts caused by unstable body position of a traditional device can be solved.

The invention relates to the technical field of image data processing, in particular to a CT image feature extraction method for gastrointestinal stromal tumor risk stratification, which comprises the following steps of: firstly, acquiring an abdominal CT plane scanning period image and a venous period image of a gastrointestinal stromal tumor patient, and performing coarse segmentation by utilizing gray scale difference of the two periods to obtain a tumor suspected area; a suspected content area is identified by analyzing gray change characteristics of the edge of the area, whether the suspected content area is a real gastrointestinal tract content (such as a contrast agent or bubbles) is further verified, the confirmed content is removed, a pure gastrointestinal stromal tumor area is finally obtained, and radiomics characteristics for risk stratification are extracted from the pure gastrointestinal stromal tumor area. According to the scheme, interference of gastrointestinal tract contents on tumor boundary judgment and internal texture analysis is effectively eliminated, so that the segmentation result is more accurate, high-quality data is provided for image omics feature extraction, the accuracy and reliability of risk layering are enhanced, and preoperative noninvasive accurate evaluation is realized.

The application discloses a CT image enhancement processing method for tumor boundary recognition, and relates to the technical field of medical image processing. The method comprises the following steps: S1, performing multi-scale decomposition on an original abdominal CT image, and obtaining a preprocessed coarse scale component and a preprocessed fine scale component through a multi-scale feature decoupling network; S2, fusing the preprocessed coarse scale component and the preprocessed fine scale component, obtaining a corresponding CT enhanced image, and obtaining an initial boundary contour of the CT enhanced image; and S3, according to the initial boundary contour, obtaining an uncertainty score corresponding to each sampling point, determining a corresponding low-confidence boundary segment, and performing decomposition and enhancement processing on the low-confidence boundary segment to obtain a final tumor boundary contour. The application can specifically correct unreliable parts in the boundary contour, significantly improves the clinical reliability and robustness of the final tumor boundary contour, and avoids overall boundary errors caused by local blurring or noise.

The application discloses a kind of medical image segmentation methods and systems for abdominal CT image, it is related to medical image processing technical field, including: obtaining three-dimensional CT body data, is divided into training dataset and inference dataset;Training data set is input into segmentation model and is trained, and the segmentation model after training is obtained;Inference data set is input into the segmentation model after training, executes segmentation step and obtains class probability graph, class label graph is obtained by processing class probability graph, finally obtains three-dimensional segmentation body;Three-dimensional segmentation body is post-processed using connected domain post-processing rule, and finally multi-class segmentation result is obtained.The application adopts the above-mentioned medical image segmentation method and system for abdominal CT image, through the joint design of training stage, inference stage and post-processing stage, improve the segmentation precision and output stability of complex boundary region, small organ region and target organ overall structure.

The invention relates to an ovarian cancer metastasis assessment local feature and global feature extraction method and device, which can improve the accuracy of ovarian cancer metastasis assessment, effectively prevent ovarian cancer metastasis and help medical personnel to discover lesions as soon as possible, so that effective treatment measures can be taken in time. The method comprises the following steps: (1) training and identifying seven key anatomical regions in an epigastric CT image; (2) performing multi-phase information fusion based on a multi-channel CNN encoder; (3) performing global feature learning and modeling on the multi-phase information by using Swin Transform, and processing the global information of the image; and (4) performing local feature learning on the multi-phase information by using a convolutional neural network (CNN), capturing detail information in each phase by using local feature extraction of the CNN, and extracting local texture, shape and edge information from the multi-phase medical image through convolution operation and nonlinear transformation.

The application discloses a liver tumor segmentation method fusing three kinds of attentions, which effectively fuses spatial attention, self-attention and edge fusion attention, and realizes accurate liver tumor segmentation. The method comprises the following steps: S1, preprocessing abdominal CT data, and dividing the data into a training set, a verification set and a test set in proportion; S2, combining double attention with convolution operation to construct double attention dynamic convolution; S3, fusing edge information and attention mechanism to construct an edge fusion attention module; S4, fusing edge supervision loss and segmentation loss to construct a multi-scale edge segmentation loss; S5, fusing double attention dynamic convolution, the edge fusion attention module and the multi-scale edge segmentation loss to construct a Triple Attention Fusion Network (TAF-Net) model; and S6, using an experimental data set to complete training, verification optimization and performance evaluation of the model. The application can adaptively adjust a convolution kernel and effectively capture edge features, effectively improves segmentation precision, and is suitable for automatic segmentation of liver tumors in CT images.

The invention discloses a colorectal cancer preoperative prognosis prediction method and device, and the method comprises the steps: calculating a systematic inflammation index and a body composition index of a target based on obtained object preoperative baseline data, the baseline data comprising basic clinical data, blood indexes and abdominal CT images; carrying out importance evaluation on the target systemic inflammation index, the body composition index and the basic clinical data, carrying out dimensionality reduction and regularization processing on the evaluated indexes, and screening to obtain a target index; and constructing a comprehensive prognosis prediction model based on the target index, and outputting a target risk score through the comprehensive prognosis prediction model so as to carry out preoperative prognosis prediction. According to the application, the scientificity, accuracy and intelligent level of preoperative risk prediction of the colorectal cancer patient are improved.

This invention discloses a method for abdominal multi-organ segmentation using a bridge encoder-decoder, implemented according to the following steps: Step 1: Preprocess the image; Step 2: Construct a two-stage segmentation framework from coarse to fine, selecting the SegNet network for the coarse segmentation stage and building the BridgeNet network for the fine segmentation stage; Step 3: Train the SegNet network using the preprocessed training data; Step 4: Segment the pre-trained training data using the trained SegNet to obtain the coarse segmentation result, then multiply it by the pre-trained training data to obtain the training data for the fine segmentation stage, and train the BridgeNet network; Step 5: Segment the test set using the trained SegNet and BridgeNet to obtain the preliminary segmentation result, and then apply connected component analysis to the preliminary segmentation result to obtain an accurate abdominal CT multi-organ segmentation map.

The invention discloses a method and a system for analyzing body components based on abdominal CT (computed tomography) scanning, which effectively solve the problem of spine confusion caused by highly similar structures of adjacent vertebral bodies by introducing a local anatomical marker on an image, and do not need to depend on a large data set to perform remote vertebral body context learning, thereby reducing the data demand and improving the accuracy of the data. And the reliability of spine segmentation is improved. According to the method, a two-stage segmentation strategy is adopted, spine segmentation and body composition segmentation are optimized respectively, the independence and precision of each segmentation task are guaranteed, the strategy can effectively improve the segmentation accuracy, and compared with an existing method, centrum confusion is reduced, and the consistency of measurement results is improved. According to the method, three-dimensional volume measurement of muscle, subcutaneous fat and visceral fat is realized, and compared with a single-slice or multi-slice two-dimensional area inference method, tissue distribution in a whole spine range can be reflected more comprehensively and accurately, so that the volume measurement error is remarkably reduced, and the reference of data is improved.

The invention discloses a metabolic syndrome image segmentation method and system based on a deep learning algorithm. The method comprises the following steps: acquiring and preprocessing an abdominal CT scanning image to be segmented; inputting the preprocessed image into a trained image segmentation model, outputting a segmentation mask of subcutaneous fat, and completing image segmentation of the subcutaneous fat; the image segmentation model is an improved ENDNet model based on U-net3 + and specifically comprises a multi-layer encoder and a multi-layer decoder structure which are sequentially connected in series, an Enhanced-Inception module is introduced into each layer of encoder, multi-scale feature information is extracted through the multi-layer encoder, and encoding features containing rich-level information are obtained; shallow layer features of the encoder and deep layer features of the decoder are fused through jump connection, the fused features are processed through the decoder, and an accurate pixel-level segmentation result is generated. According to the method, the multi-scale feature extraction capability of the model can be enhanced, and the multi-scale feature of subcutaneous fat can be fully adapted, so that the segmentation precision is improved.

The invention discloses a medical image segmentation method based on cross-shaped window self-attention and edge perception of channel enhancement, and belongs to the technical field of medical image processing. According to the method, an ECCA block (integrating cross window self-attention CSWin and channel attention SE) is designed, so that global dependency modeling and channel feature adaptive enhancement are realized while the calculation complexity is reduced (from reduced to reduced); a fuzzy organ boundary is accurately reserved by combining a lightweight edge sensing module with a depth supervision strategy; and balancing region segmentation and boundary precision through a composite loss function (Dice loss + cross entropy loss + edge loss). Experiments show that the segmentation precision of the method on a Synapse abdominal CT data set (average DSC 81.90%) and an ACDC heart MRI data set (average DSC 91.10%) is superior to that of an existing mainstream method, multi-modal medical image segmentation can be efficiently and accurately completed, and support is provided for clinical diagnosis and treatment.

The invention belongs to the technical field of medical images, and discloses a U-Net-based multi-window wide bed abdomen CT pneumoperitoneum automatic segmentation method, which comprises the following steps of S1, collecting CT image data of a patient, and performing preprocessing; s2, establishing an automatic segmentation model based on a segmentation algorithm of a U-Net network, and training the model; and S3, quantitatively optimizing the window width and the window level for the trained automatic segmentation model by adopting a grid search algorithm. According to the multi-window-width bed abdomen CT pneumoperitoneum automatic segmentation method based on the U-Net, the window width and window level parameter combination is dynamically adjusted, automatic detection and accurate segmentation of free gas in a peritoneal cavity are achieved, the pneumoperitoneum diagnosis efficiency and accuracy are improved, the missed diagnosis risk is reduced, and a reliable basis is provided for clinical decision making.

The application discloses a chest CT image multi-abnormality recognition and positioning method and system based on deep learning, and the method comprises the following steps: customizing chest and abdominal CT abnormality labels, constructing a chest CT multi-abnormality positioning labeling data set according to the abnormality labels; based on the data set, training a YOLO model, and performing abnormality detection on a single tomographic image in an input CT sequence to output a preliminary detection result with a tomographic sequence number; extracting interlayer parameters of the CT sequence, and based on the interlayer parameters and the preliminary detection result, performing interlayer correlation verification and three-dimensional feature fusion to obtain a detection result after verification and fusion; and based on the detection result after verification and fusion, generating and outputting a final detection report containing abnormal three-dimensional information. The application can start from constructing a chest CT data set for multi-class abnormality positioning labeling, utilize a target recognition and positioning network in computer vision, and train an auxiliary diagnosis model capable of simultaneously recognizing and positioning common abnormal signs of chest CT.

The application provides a kind of diabetes nephropathy auxiliary identification method and system based on CT image, it is related to medical image processing and computer-aided diagnosis technical field, the method comprises: obtaining the abdominal CT image of target object;Segment kidney, perirenal fat and body composition region of interest;Extract radiomics features;Screening to obtain target radiomics features;Input radiomics model to obtain whether target object is identified result of suffering from diabetes nephropathy.The application extracts microcosmic image markers related to renal function damage from conventional CT images by comprehensively using multiple site radiomics features, realizes non-invasive, accurate auxiliary identification of diabetes nephropathy, significantly improves detection efficiency, and provides a reliable image-aided diagnosis tool for clinic.

The application discloses a kind of liver tumor automatic identification and measurement method based on CT image, belong to medical image processing technical field, including the following steps: first, after the image pre-processing of patient abdominal CT image, input into liver component decomposition network, obtain the liver component image I1-I4 after decomposition, then liver component image I1-I4 is input into liver tumor segmentation network, and the segmentation result of tumor is output;Finally, the segmentation result is input into tumor measurement and analysis module to analyze tumor, and the detailed information of tumor is calculated.Through the above mode, the application solves the problems of poor applicability and robustness of traditional methods, difficulty in guaranteeing the accuracy of results, lack of model interpretability and other problems.The application has strong applicability and robustness, high accuracy, and the model has interpretability.

The invention discloses an abdominal CT image segmentation method based on a local-global mixed attention mechanism. The method comprises the following steps: inputting an abdominal CT image to be segmented into an LG-UNet model, and obtaining a visualization result of image segmentation; wherein the LG-UNet model is obtained based on training of a training set, and the training set comprises the CT images of the multiple organs of the abdomen and the corresponding true values of the CT images. In a model structure, global attention and local attention are calculated in parallel through a mixed attention module, and weighted fusion is carried out on the global attention and the local attention by utilizing a learnable dynamic proportionality coefficient, so that the perception capability of the model on global information and local details of an image is enhanced. And the mixed attention module is integrated in front of and behind the last down-sampling layer of the encoder of the U-Net network to form a dynamic attention adjustment module. The module uses local attention windows of different sizes in different hierarchies to accommodate image features of different hierarchies. Through the design, the model can effectively fuse local and global features, and the accuracy of abdominal CT image segmentation is remarkably improved.