Continuous convex relaxation methodology applied to retroperitoneal tumors

In this paper, two algorithms for the segmentation of tumors in soft tissues are presented and compared. These algorithms are applied to the segmentatiion of retroperitoneal tumors. Method: The algorithms are based on a continuous convex relaxation methodology with the introduction of an accumulated gradient distance (AGD). Algorithm 1 is based on two-label convex relaxation and Algorithm 2 applies multilabel convex relaxation. Results: Algorithms 1 and 2 are tested on a database of 6 CT volumes and their results are compared with the manual segmentation. The multilabel version performs better, achieving a 91% of sensitivity, 100% of specificity, 88% of PPV and 89% of Dice index. Conclusions: To the best of our knowledge, this is the first time that the segmentation of retroperitoneal tumors has been addressed. Two segmentation algorithms have been compared and the multilabel version obtains very good resultsJunta de Andalucía P11-TIC-7727Junta de Andalucía PT13/0006/003

A Novel System and Image Processing for Improving 3D Ultrasound-guided Interventional Cancer Procedures

Image-guided medical interventions are diagnostic and therapeutic procedures that focus on minimizing surgical incisions for improving disease management and reducing patient burden relative to conventional techniques. Interventional approaches, such as biopsy, brachytherapy, and ablation procedures, have been used in the management of cancer for many anatomical regions, including the prostate and liver. Needles and needle-like tools are often used for achieving planned clinical outcomes, but the increased dependency on accurate targeting, guidance, and verification can limit the widespread adoption and clinical scope of these procedures. Image-guided interventions that incorporate 3D information intraoperatively have been shown to improve the accuracy and feasibility of these procedures, but clinical needs still exist for improving workflow and reducing physician variability with widely applicable cost-conscience approaches. The objective of this thesis was to incorporate 3D ultrasound (US) imaging and image processing methods during image-guided cancer interventions in the prostate and liver to provide accessible, fast, and accurate approaches for clinical improvements. An automatic 2D-3D transrectal ultrasound (TRUS) registration algorithm was optimized and implemented in a 3D TRUS-guided system to provide continuous prostate motion corrections with sub-millimeter and sub-degree error in 36 ± 4 ms. An automatic and generalizable 3D TRUS prostate segmentation method was developed on a diverse clinical dataset of patient images from biopsy and brachytherapy procedures, resulting in errors at gold standard accuracy with a computation time of 0.62 s. After validation of mechanical and image reconstruction accuracy, a novel 3D US system for focal liver tumor therapy was developed to guide therapy applicators with 4.27 ± 2.47 mm error. The verification of applicators post-insertion motivated the development of a 3D US applicator segmentation approach, which was demonstrated to provide clinically feasible assessments in 0.246 ± 0.007 s. Lastly, a general needle and applicator tool segmentation algorithm was developed to provide accurate intraoperative and real-time insertion feedback for multiple anatomical locations during a variety of clinical interventional procedures. Clinical translation of these developed approaches has the potential to extend the overall patient quality of life and outcomes by improving detection rates and reducing local cancer recurrence in patients with prostate and liver cancer

Software and Hardware-based Tools for Improving Ultrasound Guided Prostate Brachytherapy

Minimally invasive procedures for prostate cancer diagnosis and treatment, including biopsy and brachytherapy, rely on medical imaging such as two-dimensional (2D) and three-dimensional (3D) transrectal ultrasound (TRUS) and magnetic resonance imaging (MRI) for critical tasks such as target definition and diagnosis, treatment guidance, and treatment planning. Use of these imaging modalities introduces challenges including time-consuming manual prostate segmentation, poor needle tip visualization, and variable MR-US cognitive fusion. The objective of this thesis was to develop, validate, and implement software- and hardware-based tools specifically designed for minimally invasive prostate cancer procedures to overcome these challenges. First, a deep learning-based automatic 3D TRUS prostate segmentation algorithm was developed and evaluated using a diverse dataset of clinical images acquired during prostate biopsy and brachytherapy procedures. The algorithm significantly outperformed state-of-the-art fully 3D CNNs trained using the same dataset while a segmentation time of 0.62 s demonstrated a significant reduction compared to manual segmentation. Next, the impact of dataset size, image quality, and image type on segmentation performance using this algorithm was examined. Using smaller training datasets, segmentation accuracy was shown to plateau with as little as 1000 training images, supporting the use of deep learning approaches even when data is scarce. The development of an image quality grading scale specific to 3D TRUS images will allow for easier comparison between algorithms trained using different datasets. Third, a power Doppler (PD) US-based needle tip localization method was developed and validated in both phantom and clinical cases, demonstrating reduced tip error and variation for obstructed needles compared to conventional US. Finally, a surface-based MRI-3D TRUS deformable image registration algorithm was developed and implemented clinically, demonstrating improved registration accuracy compared to manual rigid registration and reduced variation compared to the current clinical standard of physician cognitive fusion. These generalizable and easy-to-implement tools have the potential to improve workflow efficiency and accuracy for minimally invasive prostate procedures

Vascular Segmentation Algorithms for Generating 3D Atherosclerotic Measurements

Atherosclerosis manifests as plaques within large arteries of the body and remains as a leading cause of mortality and morbidity in the world. Major cardiovascular events may occur in patients without known preexisting symptoms, thus it is important to monitor progression and regression of the plaque burden in the arteries for evaluating patient\u27s response to therapy. In this dissertation, our main focus is quantification of plaque burden from the carotid and femoral arteries, which are major sites for plaque formation, and are straight forward to image noninvasively due to their superficial location. Recently, 3D measurements of plaque burden have shown to be more sensitive to the changes of plaque burden than one-/two-dimensional measurements. However, despite the advancements of 3D noninvasive imaging technology with rapid acquisition capabilities, and the high sensitivity of the 3D plaque measurements of plaque burden, they are still not widely used due to the inordinate amount of time and effort required to delineate artery walls plus plaque boundaries to obtain 3D measurements from the images. Therefore, the objective of this dissertation is developing novel semi-automated segmentation methods to alleviate measurement burden from the observer for segmentation of the outer wall and lumen boundaries from: (1) 3D carotid ultrasound (US) images, (2) 3D carotid black-blood magnetic resonance (MR) images, and (3) 3D femoral black-blood MR images. Segmentation of the carotid lumen and outer wall from 3DUS images is a challenging task due to low image contrast, for which no method has been previously reported. Initially, we developed a 2D slice-wise segmentation algorithm based on the level set method, which was then extended to 3D. The 3D algorithm required fewer user interactions than manual delineation and the 2D method. The algorithm reduced user time by ≈79% (1.72 vs. 8.3 min) compared to manual segmentation for generating 3D-based measurements with high accuracy (Dice similarity coefficient (DSC)\u3e90%). Secondly, we developed a novel 3D multi-region segmentation algorithm, which simultaneously delineates both the carotid lumen and outer wall surfaces from MR images by evolving two coupled surfaces using a convex max-flow-based technique. The algorithm required user interaction only on a single transverse slice of the 3D image for generating 3D surfaces of the lumen and outer wall. The algorithm was parallelized using graphics processing units (GPU) to increase computational speed, thus reducing user time by 93% (0.78 vs. 12 min) compared to manual segmentation. Moreover, the algorithm yielded high accuracy (DSC \u3e 90%) and high precision (intra-observer CV \u3c 5.6% and inter-observer CV \u3c 6.6%). Finally, we developed and validated an algorithm based on convex max-flow formulation to segment the femoral arteries that enforces a tubular shape prior and an inter-surface consistency of the outer wall and lumen to maintain a minimum separation distance between the two surfaces. The algorithm required the observer to choose only about 11 points on its medial axis of the artery to yield the 3D surfaces of the lumen and outer wall, which reduced the operator time by 97% (1.8 vs. 70-80 min) compared to manual segmentation. Furthermore, the proposed algorithm reported DSC greater than 85% and small intra-observer variability (CV ≈ 6.69%). In conclusion, the development of robust semi-automated algorithms for generating 3D measurements of plaque burden may accelerate translation of 3D measurements to clinical trials and subsequently to clinical care

Segmentación de tejidos con contornos difusos en imágenes radiológicas

En la presente tesis se han desarrollado dos algoritmos innovadores para la segmentación de tumores retroperitoneales en imágenes radiológicas en 3D, ambos basados en la metodología de relajación convexa. Un algoritmo hace uso de dos etiquetas y el otro implementa una metodología multietiquetas. Los algoritmos permiten a los oncólogos radioterápicos y cirujanos la selección semiautomática de los tumores para planificar los tratamientos radioterápicos y las cirugías, en los casos en los que fuera necesario. Los algoritmos desarrollados sólo requieren como entrada el contorno no preciso del tumor en un corte 2D del TAC. La principal novedad de los algoritmos radica en la introducción de la Distancia Acumulada de Gradiente de Volumen (DAGV) previa a la optimización. En ese sentido, la información del gradiente es introducida en el término regional junto con la información de la intensidad. El término de regularización, que penaliza la longitud del contorno, proporciona sólo una visión a nivel de vóxel y la DAGV a nivel local. La gran importancia de estos algoritmos también reside en que no se ha detectado en la literatura ningún estudio que se centre en la segmentación de este tipo de tumor, el tumor retroperitoneal. Con los dos algoritmos desarrollados se segmentaron 19 casos de TAC de pacientes reales compuestos por 275 cortes en total de tumores retroperitoneales. Los resultados se compararon con la selección manual de los mismos tumores proporcionados por un panel de expertos. Tras la evaluación se seleccionó el algoritmo de multietiquetas con una etapa de post-procesamiento usando un disco de 5 píxeles de radio como elemento estructural, por ser esta implementación la que proporcionó los mejores resultados. A continuación, se comparó el algoritmo con un banco de algoritmos de segmentación basados en metodologías de umbralización, Level-set basado en bordes [1] y Level-set basado en regiones [2] disponibles en la literatura. El algoritmo diseñado también se comparó con varios algoritmos de segmentación disponibles en aplicaciones comerciales para la planificación de radioterapia y de cirugía. En concreto, como planificador de radioterapia se comparó con el planificador Pinnacle [3] y como planificador de cirugía se comparó con VirSSPA [4-6] con los algoritmos de segmentación que tiene implementados basados en umbralización, crecimiento de regiones y crecimiento de regiones con paso adaptativo. Se evaluaron 24 parámetros relativos a la evaluación de la región, de la proximidad al contorno, del volumen y del tiempo computacional, y se compararon los resultados obtenidos con los resultados proporcionados por los diferentes algoritmos de segmentación de la literatura. El algoritmo multietiquetas diseñado obtuvo los mejores resultados en 20 de los 24 parámetros. En concreto, la segmentación proporcionada por el algoritmo multietiquetas desarrollado alcanzó unos resultados del 90% en Sensibilidad, 100% en Especificidad, 84% en PPV, 77% de coeficiente Jaccard, 100% Exactitud, 67% Conformidad, 87% de Sensibilidad ηsbl y de coeficiente Dice, 100% de Recall y 96% de Precisión, entre otros parámetros. Los cuatro parámetros en los que el algoritmo diseñado no obtuvo los mejores resultados fueron en el tiempo computacional y en el cálculo del volumen estimado a través del análisis de Bland-Altman. En estos cuatros parámetros, el algoritmo que proporcionó mejores resultados fue el de umbralización, pero con una leve mejoría respecto al algoritmo diseñado de multietiquetas. El coste computacional del algoritmo de umbralización es menor, porque el procesamiento es más simple. En cambio, aunque según el análisis de Bland-Altman el volumen resultante por el algoritmo de umbralización se asemeja más al volumen real, dichos volúmenes no solapan bien, dado que la Sensibilidad y Sensibilidad ηsbl, son mejores para el algoritmo diseñado. En relación a los algoritmos implementados en las soluciones comerciales de planificadores de radioterapia y cirugía, el algoritmo diseñado también proporcionó los mejores resultados en todos los parámetros analizados. Se evaluó también la variabilidad entre observadores en la delimitación manual de los tumores y se demostró que el algoritmo propuesto puede ayudar en casos difíciles de segmentar y que presentan diferentes lecturas, reduciendo por tanto la variabilidad en la práctica clínica. También se analizó la dependencia del algoritmo diseñado y seleccionado respecto a los parámetros de inicialización. Se demostró que el algoritmo es robusto a la inicialización. En otras palabras, con el algoritmo diseñado, la variabilidad entre usuarios debida a la segmentación manual de los tumores se reduce. Esto implica que, con el algoritmo, se les proporciona a los oncólogos radioterápicos un sistema de delimitación del volumen tumoral que posibilita el aumento de la uniformidad en el diseño de los tratamientos de radioterapia y, por tanto, la reducción en la variabilidad en la práctica clínica