Idioma:

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

Twinanda, Andru P. ; Shehata, Sherif ; Mutter, Didier ; Marescaux, Jacques ; de Mathelin, Michel ; Padoy, Nicolas

IEEE transactions on medical imaging, 2017-01, Vol.36 (1), p.86-97

United States: IEEE

Texto completo disponível

Citações Citado por

Enviar para

Título:
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
Autor: Twinanda, Andru P. ; Shehata, Sherif ; Mutter, Didier ; Marescaux, Jacques ; de Mathelin, Michel ; Padoy, Nicolas
Assuntos: Algorithms ; Artificial neural networks ; Automatic ; Cholecystectomy ; Computer architecture ; Computer programs ; convolutional neural network ; Databases, Factual ; Engineering Sciences ; Feature extraction ; Image recognition ; Laparoscopes ; Laparoscopic videos ; Laparoscopy ; Medical imaging ; Neural networks ; Neural Networks (Computer) ; Optimization ; phase recognition ; Surgery ; tool presence detection ; Videos ; Visualization
É parte de: IEEE transactions on medical imaging, 2017-01, Vol.36 (1), p.86-97
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.
Editor: United States: IEEE
Idioma: Inglês

Links

Voltar para lista de resultados

Anterior Resultado 10 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

Twinanda, Andru P. ; Shehata, Sherif ; Mutter, Didier ; Marescaux, Jacques ; de Mathelin, Michel ; Padoy, Nicolas

United States: IEEE

Buscando em bases de dados remotas. Favor aguardar.