Recently, deep learning methods have had a tremendous impact on computer vision applications. The results obtained were unimaginable a few years ago. The problems of greatest interest are image classification, semantic segmentation, object detection, face recognition, and so on. All these tasks have in common the necessity of having a sufficient quantity of data to be able to train the model in a suitable manner. In fact, deep neural networks have a very high number of parameters, which imposes a fairly large dataset of supervised examples for their training. This problem is particularly important in the medical field, especially when the goal is the semantic segmentation of images, both due to the presence of privacy issues and the high cost of image tagging by medical experts. The main objective of this thesis is to study new methods for generating synthetic images along with their label–maps for segmentation purposes. The generated images can be used to augment real datasets. In the thesis, in order to achieve such a goal, new fully data–driven methods based on Generative Adversarial Networks are proposed. The main characteristic of these methods is that, differently from other approaches described in literature, they are multi–stage, namely they are composed of some steps. Indeed, by splitting the generation procedure in steps, the task is simplified and the employed networks require a smaller number of examples for learning. In particular, a first proposed method consists of a two–stage image generation procedure, where the semantic label–maps are produced first, and then the image is generated from the label–maps. This approach has been used to generate retinal images along with the corresponding vessel segmentation label–maps. With this method, learning the generator requires only a handful of samples. The method generates realistic high–resolution retinal images. Moreover, the generated images can be used to augment the training set of a segmentation algorithm. In this way, we achieved results that outperforms the state–of–the–art for the task of segmentation of retinal vessels. In the second part of the thesis, a three–stage approach is presented: the initial step consists in the generation of dots whose positions indicate the locations of the semantic objects represented in the image; then, in the second step, the dots are translated into semantic label–maps, which are, finally, transformed into the image. The method was evaluated on the segmentation of chest radiographic images. The experimental results are promising both from a qualitative and quantitative point of view.

Multi-stage generation for segmentation of medical images / Giorgio Ciano. - (2022).

Multi-stage generation for segmentation of medical images

Giorgio Ciano
2022

Abstract

Recently, deep learning methods have had a tremendous impact on computer vision applications. The results obtained were unimaginable a few years ago. The problems of greatest interest are image classification, semantic segmentation, object detection, face recognition, and so on. All these tasks have in common the necessity of having a sufficient quantity of data to be able to train the model in a suitable manner. In fact, deep neural networks have a very high number of parameters, which imposes a fairly large dataset of supervised examples for their training. This problem is particularly important in the medical field, especially when the goal is the semantic segmentation of images, both due to the presence of privacy issues and the high cost of image tagging by medical experts. The main objective of this thesis is to study new methods for generating synthetic images along with their label–maps for segmentation purposes. The generated images can be used to augment real datasets. In the thesis, in order to achieve such a goal, new fully data–driven methods based on Generative Adversarial Networks are proposed. The main characteristic of these methods is that, differently from other approaches described in literature, they are multi–stage, namely they are composed of some steps. Indeed, by splitting the generation procedure in steps, the task is simplified and the employed networks require a smaller number of examples for learning. In particular, a first proposed method consists of a two–stage image generation procedure, where the semantic label–maps are produced first, and then the image is generated from the label–maps. This approach has been used to generate retinal images along with the corresponding vessel segmentation label–maps. With this method, learning the generator requires only a handful of samples. The method generates realistic high–resolution retinal images. Moreover, the generated images can be used to augment the training set of a segmentation algorithm. In this way, we achieved results that outperforms the state–of–the–art for the task of segmentation of retinal vessels. In the second part of the thesis, a three–stage approach is presented: the initial step consists in the generation of dots whose positions indicate the locations of the semantic objects represented in the image; then, in the second step, the dots are translated into semantic label–maps, which are, finally, transformed into the image. The method was evaluated on the segmentation of chest radiographic images. The experimental results are promising both from a qualitative and quantitative point of view.
2022
Monica Bianchini, Franco Scarselli
Giorgio Ciano
File in questo prodotto:
File Dimensione Formato  
Ciano_Giorgio_PhD_Thesis_SCTEMPLATE.pdf

accesso aperto

Tipologia: Tesi di dottorato
Licenza: Open Access
Dimensione 34.74 MB
Formato Adobe PDF
34.74 MB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1274075
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact