Multiatlas-Based Segmentation Editing With Interaction-Guided Patch Selection and Label Fusion

We propose a novel multi-atlas based segmentation method to address the segmentation editing scenario, where an incomplete segmentation is given along with a set of existing reference label images (used as atlases). Unlike previous multi-atlas based methods, which depend solely on appearance features, we incorporate interaction-guided constraints to find appropriate atlas label patches in the reference label set and derive their weights for label fusion. Specifically, user interactions provided on the erroneous parts are first divided into multiple local combinations. For each combination, the atlas label patches well-matched with both interactions and the previous segmentation are identified. Then, the segmentation is updated through the voxel-wise label fusion of selected atlas label patches with their weights derived from the distances of each underlying voxel to the interactions. Since the atlas label patches well-matched with different local combinations are used in the fusion step, our method can consider various local shape variations during the segmentation update, even with only limited atlas label images and user interactions. Besides, since our method does not depend on either image appearance or sophisticated learning steps, it can be easily applied to general editing problems. To demonstrate the generality of our method, we apply it to editing segmentations of CT prostate, CT brainstem, and MR hippocampus, respectively. Experimental results show that our method outperforms existing editing methods in all three data sets

Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation

A novel label fusion method for multi-atlas based image segmentation method is developed by integrating semi-supervised and supervised machine learning techniques. Particularly, our method is developed in a pattern recognition based multi-atlas label fusion framework. We build random forests classification models for each image voxel to be segmented based on its corresponding image patches of atlas images that have been registered to the image to be segmented. The voxelwise random forests classification models are then applied to the image to be segmented to obtain a probabilistic segmentation map. Finally, a semi-supervised label propagation method is adapted to refine the probabilistic segmentation map by propagating its reliable voxelwise segmentation labels, taking into consideration consistency of local and global image appearance of the image to be segmented. The proposed method has been evaluated for segmenting the hippocampus in MR images and compared with alternative machine learning based multi-atlas based image segmentation methods. The experiment results have demonstrated that our method could obtain competitive segmentation performance (average Dice index > 0.88), compared with alternative multi-atlas based image segmentation methods under comparison. Source codes of the methods under comparison are publicly available at www.nitrc.org/frs/?group_id=1242

Towards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks

Segmentation of anatomy on abdominal CT enables patient-specific image guidance in clinical endoscopic procedures and in endoscopy training. Because robust interpatient registration of abdominal images is necessary for existing multi-atlas- and statistical-shape-model-based segmentations, but remains challenging, there is a need for automated multi-organ segmentation that does not rely on registration. We present a deep-learning-based algorithm for segmenting the liver, pancreas, stomach, and esophagus using dilated convolution units with dense skip connections and a new spatial prior. The algorithm was evaluated with an 8-fold cross-validation and compared to a joint-label-fusion-based segmentation based on Dice scores and boundary distances. The proposed algorithm yielded more accurate segmentations than the joint-label-fusion-ba sed algorithm for the pancreas (median Dice scores 66 vs 37), stomach (83 vs 72) and esophagus (73 vs 54) and marginally less accurate segmentation for the liver (92 vs 93). We conclude that dilated convolutional networks with dense skip connections can segment the liver, pancreas, stomach and esophagus from abdominal CT without image registration and have the potential to support image-guided navigation in gastrointestinal endoscopy procedures

Learning to segment fetal brain tissue from noisy annotations

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI

Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks

Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deeplearning- based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the GI tract (esophagus, stomach, duodenum) and surrounding organs (liver, spleen, left kidney, gallbladder). We directly compared the segmentation accuracy of the proposed method to existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 vs. 0.71, 0.74 and 0.74 for the pancreas, 0.90 vs 0.85, 0.87 and 0.83 for the stomach and 0.76 vs 0.68, 0.69 and 0.66 for the esophagus. We conclude that deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures

Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces