A crucial step of the radiotherapy workflow is the segmentation of the tumors. Currently, this is done manually, which is very time-consuming and therefore puts a lot of burden in the clinical... Show moreA crucial step of the radiotherapy workflow is the segmentation of the tumors. Currently, this is done manually, which is very time-consuming and therefore puts a lot of burden in the clinical workflow. Deep learning techniques, currently state of the art for computer vision tasks, are a potential solution to speed up the process. Even though they have been applied to segment other structures of interest for the RT workflow, they still underperform for the case of tumors. Furthermore, there is limited research in automatic segmentation of tumors for the particular case of MRI, even though this imaging modality presents better soft tissue contrast and it is therefore ideal to visualize tumors. The goal of this thesis was to develop automatic segmentation techniques for tumors on MRI images that deliver clinically acceptable segmentations. The different automatic segmentation methods were applied in two different tasks: the automatic segmentation of the oropharyngeal primary tumor in multiparametric diagnostic MRI images and the automatic segmentation of the cervical cancer gross tumor volume in the MRI images of the brachytherapy treatment images. Show less
Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical... Show moreManual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality. For this purpose, we present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs). The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention gates in each dense block to selectively concentrate on determinant feature maps and regions. Dilated convolutional layers are used to manage GPU memory and increase the network receptive field. We collected a dataset of 792 scans from 288 distinct patients including varying anatomies with air pockets, feeding tubes and proximal tumors. Repeatability and reproducibility studies were conducted for three distinct splits of training and validation sets. The proposed network achieved a DSC value of 0.79 +/- 0.20, a mean surface distance of 5.4 +/- 20.2mm and 95% Hausdorff distance of 14.7 +/- 25.0mm for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone. Our code is publicly available via https://github.com/yousefis/DenseUnet_Esophagus_Segmentation. Show less
Chiesa-Estomba, C.M.; Ravanelli, M.; Farina, D.; Remacle, M.; Simo, R.; Peretti, G.; ... ; Piazza, C. 2020
Introduction The modern availability in daily practice of different DICOM viewers allows physicians to routinely evaluate computed tomography (CT) and magnetic resonance (MR) scans of patients in... Show moreIntroduction The modern availability in daily practice of different DICOM viewers allows physicians to routinely evaluate computed tomography (CT) and magnetic resonance (MR) scans of patients in the pre-, intra-, and postoperative settings. Their systematic use, together with a close surgeon-radiologist cooperation, may greatly improve outcomes of patients to be treated by transoral microsurgery for laryngeal cancer. Materials and methods We herein propose guidelines for systematic evaluation of CT/MR images taken from patients affected by supraglottic and glottic cancer to be treated by transoral microsurgery. Results A methodical, step-by-step approach focused on laryngeal anatomy, systematically looking at each true and false vocal folds, anterior commissure, laryngeal ventricle, subglottic area, epiglottis, thyroid, cricoid, and arytenoid cartilages, posterior commissure, crico-arytenoid unit, paraglottic and pre-epiglottic spaces, and possible extra-laryngeal extension is proposed. This checklist may be useful before imaging performance (to focus on specific issues to be detailed by the radiologist), as well before and during surgery for the specific evaluation of details to be cleared during transoral microsurgery. Conclusion Detailed preoperative evaluation of supraglottic and glottic anatomy is essential prior to any transoral approach for neoplastic disease. The proposed imaging checklist described herein represents a step-by-step guide to surgeons performing this kind of interventions and an aid in achieving a meticulous approach from a surgical perspective. Show less
