METHODOLOGY TO ASSESS AUTOMATED ATLAS-BASED SEGMENTATIONS AND INTER- AND INTRA-OBSERVER VARIABILITY IN THE DELINEATION OF THE PROSTATE BED

The purpose of the study was to assess the accuracy, validity and time-savings of automated atlas-based segmentations (AABS). Further, the study assessed the inter- and intra­ observer variability in the delineation of the prostate bed (PB) and the five regions of interest for postoperative conformal radiotherapy in prostate cancer patients. Finally, the study reports on the development of an appropriate methodology for similar studies. Seventy-five DICOM Computed Tomography (CT) datasets were obtained to create the prostate bed atlas and another five datasets were retrospectively contoured by the atlas builder, the expert panel and the AABS tool. Consensus segmentations (CS) were also generated. The mean dice similarity coefficient comparing the edited AABS and CS was 0.67, 0.88, 0.93, 0.92, 0.54 and 0.78 for the PB, bladder, left- and right femoral head, penile bulb and rectum, respectively. Significant inter­ observer variation was observed in the PB and bilateral femoral heads. Significant time savings were obtained using the average AABS editing time (p = 0.003) versus the manual contouring time. We successfully developed a methodology and validated the AABS tool for routine clinical use

Auto-Segmentation of Target Volume and Organs-at-risks for Radiotherapy in Breast Cancer patients

목적: 유방암 방사선 치료에서 치료 체적에 대한 정확한 타겟 그리기는 중요하다. 하지만 방사선 치료 계획 과정에 타겟 그리기는 의료진의 부담을 주고 있으며, 의료진 간의 편차는 존재하고 있다. 본 연구에서는 Deep learning-based auto-segmentation (DLBAS)의 성능을 atlas-based segmentation solutions (ABAS)와 비교하고, 임상 의사의 관점에서 유용성을 평가하고, 최종적으로 외부 타당도 조사를 통하여 유방암 방사선 치료에서 자동 구획화의 가능성을 규명하고자 한다. 대상 및 방법: 유방암 방사선 치료 체적과 정상장기들에 대하여 한 명의 연구진에 의하여 구획화 정보를 생성하였다. Convolutional neural network 알고리즘을 이용하여 auto-contours를 생성하였고, Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD)를 이용하여 ABAS와 비교하였다. DLBAS에 의해 생성된 auto-contours의 질적인 평가를 조사하였고, manual contours와 방사선 치료 선량-체적 히스토그램을 비교하여 주요 선량평가분석을 시행하였다. 마지막으로 2개 기관의 11명의 전문가에게 manual contour를 그릴 것을 요청하여 데이터를 수집하였다. 외부 위원회를 통해 가장 최적의 치료 체적을 선정하였고, 나머지 10명의 contour와 DLBAS에 의해 생성된 auto-contour의 성능을 비교하여 순위 평가를 시행하였다. 결과: 제안된 DLBAS 모델은 대부분의 체적 (특히, 치료 체적과 심장 세부구조)에서 ABAS보다 더 일관된 결과와 높은 DSC와 낮은 HD 결과 값을 보였다. ABAS는 연조직의 정상장기와 조영제를 쓰지 않은 새로운 데이터 셋에서 DLBAS에 비해, 제한적인 성능을 보였다. 질적 평가를 위한 설문조사가 시행되었고, 중위수 8점으로 manual contour와 auto-contour 사이의 차이가 크지 않다고 대답하였으며, 임상에서 도움이 될 것으로 답변하였다. 또한 선량평가 분석 결과에서 차이는 미미하였다. 외부 검증 결과, 9개의 정상장기를 그리는데 평균 37분이 걸렸고, DLBAS는 6분이 걸렸다. Auto-contour는 전체 12개 중 1위 manual contour와 비교하였을 때 가장 DSC상 차이가 적었으며, HSD상 2번째로 차이가 적었다. 정상장기에서 가장 편차가 높았던 부위는 유방이었다. 결론: 유방 방사선 치료 계획에서 DLBAS의 실현가능성은 이번 연구에서 다각도로 검증되었다. 의료진의 최종 수정 과정은 필수적이지만, 앞으로 DLBAS는 방사선 치료를 도울 수 있는 훌륭한 가능성을 보여주었다.open박

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review

Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications

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

Automatic Segmentation of the Mandible for Three-Dimensional Virtual Surgical Planning

Three-dimensional (3D) medical imaging techniques have a fundamental role in the field of oral and maxillofacial surgery (OMFS). 3D images are used to guide diagnosis, assess the severity of disease, for pre-operative planning, per-operative guidance and virtual surgical planning (VSP). In the field of oral cancer, where surgical resection requiring the partial removal of the mandible is a common treatment, resection surgery is often based on 3D VSP to accurately design a resection plan around tumor margins. In orthognathic surgery and dental implant surgery, 3D VSP is also extensively used to precisely guide mandibular surgery. Image segmentation from the radiography images of the head and neck, which is a process to create a 3D volume of the target tissue, is a useful tool to visualize the mandible and quantify geometric parameters. Studies have shown that 3D VSP requires accurate segmentation of the mandible, which is currently performed by medical technicians. Mandible segmentation was usually done manually, which is a time-consuming and poorly reproducible process. This thesis presents four algorithms for mandible segmentation from CT and CBCT and contributes to some novel ideas for the development of automatic mandible segmentation for 3D VSP. We implement the segmentation approaches on head and neck CT/CBCT datasets and then evaluate the performance. Experimental results show that our proposed approaches for mandible segmentation in CT/CBCT datasets exhibit high accuracy

Contour-Driven Atlas-Based Segmentation

We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images